How to Build a Marketing AI Agent in 7 Steps

Introduction 

AI is rapidly reshaping marketing by automating tasks, uncovering insights, and personalizing outreach. A marketing AI agent is essentially a co-pilot for marketing teams – a conversational assistant that can brainstorm content, analyze campaign data, draft emails, or segment audiences on command. It works by combining an LLM (Large Language Model, a type of AI that understands and generates text) with your marketing data and tools, all accessible through a familiar ChatGPT-style chat interface. The twist? Instead of just replying with text, this agent can display interactive results using Generative UI (GenUI) – live charts, tables, forms, and other elements that you can engage with. For example, ask it for last quarter’s lead stats and it could respond with a summary and a bar chart you can click or filter. This is powered by C1 by Thesys, the Generative UI API that turns LLM outputs into working UI components in real time. In short, building an AI marketing agent means pairing a smart language model with a dynamic AI UI that feels like having a marketing expert on call 24/7. Thesys – the Generative UI company – makes this easier than ever by providing the infrastructure (C1 API, SDKs, etc.) to generate the Agent UI automatically, so you can focus on defining your agent’s knowledge and goals.

Key Takeaways: 7 Steps at a Glance

1. Define Goals and Guardrails: Set the marketing objectives and rules that shape your AI agent’s tone, tasks, and boundaries.
2. Choose an LLM: Select a language model (e.g., GPT-4 or Claude) that best understands marketing queries and jargon.
3. Connect Your Data: Integrate the agent with your marketing content – FAQs, campaign data, CRM – to ground its answers in real facts.
4. Add Tools & Integrations: Enable the agent to perform actions (like sending an email or pulling analytics) by hooking into marketing apps via APIs.
5. Implement Generative UI (GenUI): Use C1 by Thesys to let the agent present answers as live charts, forms, and other interactive components for a rich AI UX.
6. Test and Refine in Playground: Prototype your marketing AI agent in the Thesys Playground for quick iterations instead of coding from scratch (optional).
7. Deploy and Monitor: Launch your agent via the C1 Embed in the Thesys Management Console, then track usage, accuracy, and privacy to continually improve.

What Is a Marketing AI Agent?

A marketing AI agent is a smart assistant designed to help with marketing tasks. Think of it as a virtual marketing analyst or copywriter that converses with you. You ask questions or give instructions in plain language, and it responds with useful outputs. For example, you might ask, “Generate a summary of our social media campaign performance,” and the agent will reply with key insights – it could highlight engagement metrics in text and even produce an interactive chart of clicks and conversions. The agent operates in a ChatGPT-style interface (a familiar chat window), making it easy to use.

Typical inputs and outputs: You provide inputs like questions (“What’s our best performing ad copy this week?”), tasks (“Draft an email for our new product launch”), or data queries (“Show me website traffic by source for July”). The agent processes these using an LLM “brain” and your marketing data. The outputs can be plain answers or rich Agent UIelements: e.g. a bullet list of top channels, a form to refine audience targeting, or a table of A/B test results. The goal is to feel like a conversation with a knowledgeable colleague who can not only talk about marketing but also present information in the most helpful format.

By acting as a co-pilot, a marketing AI agent saves you time on research and analysis, and provides consistency in answers. It’s always available to support brainstorming or decision-making. Instead of digging through spreadsheets or waiting on reports, you can chat with the agent and get instant answers – often with visual aids. In short, it turns complex marketing data and workflows into a simple dialogue, with the agent handling the heavy lifting in the background.

The Stack: What You Need to Build a Marketing AI Agent

Building an AI agent for marketing involves stitching together several layers of technology. If you’re wondering how to build a marketing AI agent, it helps to think of a stack of components – from the underlying AI model, to your data sources, up to the user interface where marketers will actually interact with the agent. Below is an overview of the end-to-end stack, tailored for marketing use cases. We’ll then dive into each layer in detail, including best practices and tools.

Stack Overview

Order	Layer	Purpose (one-line)	Example Tools / Alternatives
1	Goals, Prompts & Guardrails	Define agent’s mission, style, and limits	Prompt templates (OpenAI/Anthropic guides), eval checklists (OpenAI Evals, LangSmith)
2	LLM Brain	Core AI model that understands queries	GPT-4 (OpenAI), Claude (Anthropic), PaLM (Google), Azure OpenAI
3	Knowledge & Data	Marketing content for grounding answers	Document retrieval (LangChain), Vector DBs (Pinecone, Weaviate), search (Elasticsearch)
4	Actions & Integrations	Connect to apps for doing tasks	CRM/Ads APIs (Salesforce, HubSpot), workflow automation (Zapier, Make)
5	Generative UI (GenUI)	Interactive Agent UI that adapts on the fly	C1 by Thesys (GenUI API + React SDK) – dynamic UI generation instead of static templates
6	Deployment & Monitoring	Launch the agent and ensure it runs well	Hosting (C1 Embed via Thesys Console), Observability (OpenTelemetry), Security (OWASP guidelines)

Now let’s explore each layer and see how they come together when you build a marketing AI agent. We’ll use a running example: imagine we’re creating a marketing assistant named “Marko” for an e-commerce company’s marketing team, which can answer team questions, generate content, and provide campaign insights.

1. Goals, Prompts, and Guardrails

What this layer is

This is the instruction and policy layer for your agent. It defines what the marketing AI agent should do (and not do), the tone it should use, and how it handles certain situations. Essentially, it’s like the rulebook and personality outline for “Marko.” We craft this through initial prompts (like system prompts that set the agent’s role) and guardrails (safety or compliance rules).

Function

• Scope and Role: Establishes the agent’s mission (e.g. “Help plan and analyze marketing campaigns”) and persona (friendly, professional, brand-aligned).
• Guidance: Provides example prompts or styles for the agent’s responses, ensuring consistency. For instance, you might include a template for how to answer a product FAQ versus how to draft an email.
• Safety & Compliance: Sets don’ts and limits – topics to avoid (e.g. no commenting on legal or HR matters), privacy rules (don’t reveal personal data), and fallback behaviors when unsure (“If you don’t know, say you will research and get back.”).
• Quality Check: This layer can include test criteria for success. For example, after building the agent, you might run simple evaluation prompts (using frameworks like OpenAI Evals or LangSmith) to verify the agent follows the rules and produces helpful answers.

Alternatives

• Prompt Orchestration Tools: You can manually create a prompt, or use libraries like LangChain for prompt templates. For guidance, check the [OpenAI Prompting Guide] and Anthropic Prompting docs, which offer best practices for effective prompts.
• Evaluation Frameworks: Beyond just testing by hand, consider automated evals. OpenAI Evals (an open-source eval framework) and LangSmith (LangChain’s testing suite) let you systematically evaluate outputs against success criteria. They aren’t required, but as your marketing agent grows, they help catch issues (like an off-brand tone or a forbidden phrase) early.
• Safety Layers: Some LLM providers (OpenAI, Anthropic) offer built-in content filters or you can use tools like Microsoft’s Azure AI content moderation. These act as an additional guardrail by blocking or reviewing potentially sensitive outputs.

Best practices

• Define High-Impact Tasks: Identify 3–5 core things your marketing agent should excel at (e.g. “summarize campaign results,” “suggest blog topics,” “answer product FAQs”). Build your prompts and examples around these to anchor the agent’s expertise.
• Incorporate Brand Voice: Provide a few example responses that showcase the desired tone – if your brand voice is witty yet professional, write a sample Q&A that illustrates this for the agent to mimic.
• Explicit Do’s and Don’ts: Clearly list instructions like “Do use a reassuring tone when giving advice” and “Do not make up data if unknown – ask the user to provide it or say you’ll retrieve it.” These guidelines help the agent maintain trust.
• Create a Prompt Checklist: Make a short checklist for your team to review the agent’s answers periodically. For example: Does it sound on-brand? Is the info correct and cited if needed? Is no confidential info revealed? Use this to refine the prompts or rules weekly, especially early on.
• Log Failures: Keep a log (even a simple spreadsheet) of any mistakes or bad outputs the agent produces (e.g., it gave outdated campaign info or a weird tone in one answer). Each week, review these and update your instructions or add a new guardrail to prevent repeats.

Example for marketing

For our “Marko” marketing agent, we set a system message like: “You are Marko, a marketing assistant for an e-commerce company. Your goal is to help the marketing team with insights, content ideas, and campaign analysis. Use a friendly, knowledgeable tone and concise answers. Include data when relevant. If a request is unrelated to marketing or you are unsure, politely state you can’t assist.” We also add guardrails: “Do not reveal individual customer personal data or internal-only campaign secrets. Do not give legal or financial advice.” Now Marko knows its role and limits from the get-go.

2. LLM Brain

What this layer is

This is the AI model at the heart of your agent – often an advanced Large Language Model. It’s the brain that actually reads the questions and generates answers. Think of models like GPT-4, which can understand complex queries and produce human-like text, or Anthropic’s Claude. The LLM is what gives your marketing agent its language fluency and reasoning abilities.

Function

• Language Understanding: Interprets user questions, even if they’re phrased in casual or complex ways. For example, if a marketer asks, “What were our top three channels by ROI last month?”, the LLM grasps the intent (seeking channel ROI ranking) even if the phrasing isn’t a direct database query.
• Reasoning and Context Integration: The model can take the user’s question plus any provided context (like relevant marketing data retrieved in the previous layer) and reason through it. It figures out how to combine that context into a coherent answer.
• Generation of Answers: Drafts the actual response text. A strong LLM will produce a clear explanation or recommendation – perhaps a short paragraph analyzing the ROI, followed by a suggestion on where to invest more. If LLM UI components are enabled (via GenUI), the model might also decide to output a a Thesys DSL snippet for, say, a bar chart component along with the text, making the answer interactive.
• Adaptability: The LLM’s parameters (often billions of them) enable it to adapt to various topics. With a bit of prompt tuning (from Layer 1), it can switch style for different tasks – e.g., more formal when generating a report vs. more casual when brainstorming a social media caption.

Alternatives

• OpenAI Models: ChatGPT/GPT-4 is a popular choice for its versatility in marketing tasks. If you use the OpenAI API, you get access to powerful models that have seen a lot of marketing content in training.
• Anthropic Claude: Known for its friendly tone and large context window. Claude can be great for longer marketing documents or strategy discussions since it can handle more input text at once.
• Google PaLM or others: Google’s models (like PaLM 2, used in Vertex AI) or Meta’s Llama 2 are alternatives, especially if you require specific hosting or cost considerations. Azure OpenAI is an option if you operate in the Microsoft cloud environment and want enterprise-managed instances of OpenAI models.
• Domain-Specific Models: There aren’t major LLMs specifically for marketing yet (most are general), but you could consider fine-tuning a model on your industry’s marketing data. However, that’s advanced and often unnecessary – general models, guided well, do the job for most marketing needs.

Best practices

• Start General, Adjust Settings: Begin with a strong general model (like GPT-4) before considering anything fancy. Often, tweaking parameters is enough. For instance, set the temperature lower (e.g. 0.2–0.5) for factual marketing questions to get more accurate, consistent answers, and a bit higher when you want creative output (like ad copy ideas).
• Inject Marketing Context: In your initial prompts or system message, you can preload some marketing terminology or data formats. E.g., “You are familiar with marketing KPIs like CTR, CPC, ROI” – this nudges the model to use these terms properly. Or provide an example table format for campaign results so it follows that style.
• Monitor Outputs Early: As you test, pay attention to any mistakes the model makes. Does it misunderstand “ROI” or mix up metrics? Capture those and adjust your prompts or supply clarifications. Sometimes adding a simple line like “ROI refers to Return on Investment = (Revenue – Cost)/Cost in %” helps the model avoid confusion.
• Iterate with Few-Shot Examples: If the model struggles with a certain type of question, give it an example in the prompt. For instance, show a QA pair: Q: “How did our email open rates change after Campaign X?” A: “Open rates increased from 5% to 8% (a 60% lift) after Campaign X.” This teaches the model the level of detail you expect.
• Stay Updated: Model capabilities improve rapidly. Keep an eye on new releases – a future model might handle your marketing niche (say, B2B SaaS marketing language) even better. Also, ensure your model’s knowledge is up-to-date or compensate with current data (via the Knowledge layer) so it doesn’t rely on outdated training info.

Example for marketing

We choose GPT-4 as Marko’s brain for its strong performance. In practice, when someone asks Marko, “Which ad had the best click-through rate in our last campaign?”, GPT-4 interprets this, maybe reformulates it internally as “user wants the top-performing ad by CTR for campaign X”, and once the data is provided from the next layer, it drafts an answer. If our Generative UI layer is active, GPT-4 might return not just “Ad B had the highest CTR at 12%” in text, but also a small bar chart comparing CTRs for Ads A, B, and C by including a chart component specification in the output. We’ll see more about that in the GenUI layer, but it’s the LLM that decides what to output – text vs. table vs. chart – based on its understanding of the question and the data.

3. Knowledge and Data

What this layer is

This is the grounding data for your agent – essentially, the marketing knowledge base it can draw from so that its answers are factual and up-to-date. Out of the box, an LLM knows a lot (from pre-training) about general marketing concepts and even some public statistics. But it won’t know specifics about your company’s campaigns, products, or analytics unless you provide that info. This layer connects your agent to those data sources: documents, FAQs, dashboards, etc. It’s often implemented via a retrieval system that finds relevant snippets of text or data when a question is asked.

Function

• Data Retrieval: When a user asks something, this layer searches your marketing data for relevant content. For example, if the question is about “Q3 campaign performance,” the system might pull up the Q3 campaign report or a snippet from a marketing analytics database.
• Knowledge Base: It can include various sources – marketing FAQs, product catalogs, past campaign summaries, customer feedback logs, website analytics, social media stats, CRM data, and so on. These are often indexed in a vector store (a database that enables semantic search), so even if the question wording doesn’t exactly match a document title, the agent can find related info by meaning.
• Context Injection: The retrieved data is fed into the LLM as additional context (usually in the prompt) so that the model’s answer stays accurate. If Marko is asked about “July website traffic,” the system might retrieve a line like “July 2025: 1.2 million visits (15% via email, 30% via social...)" from a Google Analytics export, and provide that to GPT-4. The model then uses it to craft the answer, ensuring the numbers match the actual data rather than the model guessing.
• Freshness and Accuracy: This layer ensures your agent isn’t stuck with only its training data (which might be old). You can update the knowledge sources any time – upload the latest campaign results, or plug into a live database – so the agent always references the newest facts. It’s crucial for marketing, where data changes daily and you don’t want to use last year’s stats by mistake.

Alternatives

• Retrieval Libraries: You can build this with tools like LangChain or LlamaIndex that simplify connecting LLMs to external data. They let you index documents and then query them when needed.
• Vector Databases: For scalable semantic search, vector DBs like Pinecone or Weaviate are popular. They store embeddings of your text data, so the agent can fetch, say, the top 3 most relevant chunks of text for a query. This is how Marko might find “the section of the Q3 report that mentions CTR”.
• Traditional Search/DB: You can also use more classic approaches: e.g., an Elasticsearch index over your docs, or even just hitting an internal knowledge base API. Some teams use OpenSearch (an open-source search engine) to similar effect. The key is that some search happens rather than expecting the AI to know everything.
• Structured Data Connectors: If your data is in SQL databases or tools like Google Analytics, you might integrate via specific connectors or APIs. For example, use a snippet of code or a service that when asked “website traffic July” actually queries your Google Analytics API and returns the number. This can then be given to the LLM. (This crosses into the Tools layer in some cases, but there’s overlap – knowledge can be unstructured text or a direct data query.)

Best practices

• Prioritize Sources: Don’t try to load every piece of marketing data at once. Start with the top 10–20 most useful sources. Common picks: product FAQs (for product-related queries), recent campaign performance summaries, your marketing strategy docs, and maybe a selection of blog posts or whitepapers if the agent is to repurpose content. High-value data means the agent more often finds a good answer without hallucinating.
• Metadata Tagging: If you have varied content, tag it with relevant info. For instance, label documents by region, product line, or date. Then if a user asks “European campaign ROI”, the system can prioritize docs tagged “Europe”. Many retrieval systems allow metadata filtering, so structure your data (e.g., each vector could have fields like region: EU or doc_type: social_report).
• Regular Updates: Schedule a cadence to update the knowledge. Marketing info gets stale fast. You might set a monthly refresh where you add the latest reports and remove or archive outdated ones. If you connect to live sources (like an analytics database or CMS), that works too – just ensure the pipeline feeding the agent stays healthy.
• Privacy and Access Control: Marketing agents might handle sensitive info (ad spend, customer data). Implement access controls: e.g., certain data sources only used if the user is authorized. Also consider redaction – if some documents have personal data, either don’t include those or use a preprocessing step to mask names/emails, so the AI doesn’t accidentally expose them.
• Test for Truthfulness: After hooking up knowledge, test a few factual queries with and without the data to ensure the agent is using it. You want it to quote the real numbers, not make them up. If it ever says something incorrect that was in your docs, double-check if the retrieval worked properly or if the prompt can be improved (e.g., “If relevant data is provided, use it directly in your answer”).

Example for marketing

We feed Marko a chunk of our marketing data: the Q3 2025 marketing performance report (PDF), a CSV export of recent email campaign metrics, a list of product descriptions, and our brand style guide (for messaging consistency). When I ask, “How many website visits did we get from our summer email campaign?”, the system will search the vector store for “summer email campaign visits”. It finds a line in the email campaign CSV that says “Summer Promo Email – Visits: 50,000”. It also finds a snippet from the Q3 report mentioning that campaign’s impact. These pieces of text are then provided to the LLM. As a result, Marko’s answer might be: “Our summer email campaign brought in approximately 50,000 website visits, according to the Q3 report, making up about 10% of that month’s total traffic.” The agent didn’t “know” that offhand – it pulled it from our data, ensuring accuracy.

4. Actions and Integrations

What this layer is

While answering questions is great, a marketing AI agent can go further – it can perform tasks on your behalf. This layer equips the agent with the ability to take actions via integrations with other tools and systems. In a marketing context, that means connecting the agent to things like your email marketing platform, CRM, analytics dashboards, social media schedulers, etc. Instead of just telling you an insight, the agent could, for example, draft and send an email, create a campaign ticket, or pull real-time data from a system. These capabilities transform the agent from a passive analyst into an active assistant.

Function

• Tool Use: The agent recognizes when a user request requires using an external tool. For instance, if you say “Schedule a LinkedIn post about our new product next Monday”, the agent could use a social media API integration to actually schedule that post, rather than just saying “Sure, I would do that.”
• Function Calls: Many modern AI systems support a mechanism (like OpenAI’s function calling) where the model can output a structured request to use a specific function/API. This layer includes definitions of what the agent can do (e.g., a function schedulePost(platform, content, date) or runGoogleAdsReport(parameters)). When the LLM “decides” it needs to act, it produces a function call that the system then executes.
• Workflow Orchestration: For more complex sequences, you might integrate with services like Zapier or Make (Integromat). The agent could trigger a Zapier workflow – say, updating a Google Sheet of campaign results or sending a Slack alert. In effect, the agent’s AI brain hands off tasks to these integrations to get stuff done.
• Result Integration: After an action is taken or data fetched, the result comes back to the AI agent which then incorporates it into its response. If Marko runs a live query (through a function) to get today’s ad spend, the numeric result is returned and the agent can include that figure in its answer to the user.

Alternatives

• Direct APIs: You can hand-code the integrations for your specific needs. For example, use the Salesforce Marketing Cloud API or HubSpot API to allow the agent to create or retrieve campaign records. If coding, you define how the agent invokes these (could be via function calling or a custom middleware).
• No-Code Platforms: Zapier Platform and Make (formerly Integromat) let you set up multi-step workflows triggered by webhooks. Your agent could call a webhook URL with details, and Zapier does the rest (e.g., if agent calls “CreateTicket(title, description)”, a Zap could create a task in Asana or ticket in Jira for the design team).
• Built-in Tool Use Frameworks: If you’re using OpenAI, their function calling system is a straightforward way to expose actions. Anthropic has a concept called “MCP” (Model-Controlled Protocol) for tools. There are also agent frameworks in LangChain which let you define tools (like a Search tool, a Calculator, etc.) – you could adapt those to marketing tools.
• Start Read-Only: Note that it’s often wise to begin with read-only actions. For example, allow the agent to fetch data (like “getAnalyticsReport(metric, date_range)”) before letting it do irreversible writes (like sending an email). This way you test the waters safely.

Best practices

• Phased Enablement: Initially, maybe just give the agent the ability to retrieve information (like querying live data). Once you trust its performance, then add write actions (like posting content or sending messages), possibly under supervision. For instance, the agent drafts a social post and asks for confirmation before actually scheduling it.
• Validate Inputs: When the agent is about to execute an action, validate the parameters. If Marko tries to call sendEmail(recipient="all_customers", content="..."), ensure that’s allowed and properly formatted. Use schemas for function inputs if possible – OpenAI’s function definitions let you enforce data types (like date must be YYYY-MM-DD), so the AI will format its calls correctly.
• Logging and Auditing: Keep a log of every action taken (time, user request that led to it, what was done). This is critical for debugging and trust. If the agent accidentally posted a wrong promo code, you want to trace how and why. Also, audit logs help with compliance – you can show who/what triggered changes in marketing systems.
• User Confirmation for Sensitive Actions: For potentially risky operations (like spending budget, sending a blast to customers), have the agent ask “Are you sure you want to proceed?” or require a human to click approve in the interface. This human-in-the-loop checkpoint can prevent costly mistakes.
• Graceful Fallbacks: If an integration fails (API down, or agent provided wrong parameters), design the agent to handle it. It should catch errors and tell the user something like “Hmm, I couldn’t connect to the email service. Let me try that again later or you might need to check the API key.” This is better than silently failing. Often, the integration layer (in your code) can return a friendly error message for the AI to relay.

Example for marketing

We integrate Marko with a few tools: the Twitter API (so it can draft tweets), our email marketing platform (through a function sendNewsletterDraft(subject, content) that actually creates a draft campaign), and Google Analytics (read-only, to pull live metrics via a function). Now, when I say: “Marko, send a thank-you promo code email to everyone who attended our webinar today.”, Marko will use the webinar attendee list from our CRM (via integration), craft an email, and with approval, send it using the email platform’s API. It might respond: “I’ve drafted an email offering 20% off for webinar attendees. Ready to send it to 150 contacts now – shall I proceed?” If I confirm, Marko calls the sendNewsletterDraft function and then replies with “✅ Email sent to 150 webinar participants!”. Under the hood, Marko executed an action in our system – a huge time saver compared to doing it manually. And because we set confirmation and logging, we maintain control and oversight of these agent-initiated actions.

5. Generative UI (GenUI)

What this layer is

This is the presentation layer – the interface that the user actually sees and interacts with. In a traditional chatbot, the UI is static (just chat bubbles). But with Generative UI (GenUI), the interface itself is dynamic and created by the AI in real time. Instead of pre-defining every button or chart, you let the AI’s output specify what UI components to show. The flagship solution here is C1 by Thesys, a Generative UI API and SDK that works with any LLM. Essentially, the AI agent can design parts of its own interface on the fly to best communicate the answer. If a picture is worth a thousand words, GenUI lets the agent show that picture (or table, or form) rather than only describing it.

Function

• Adaptive Response Rendering: The GenUI layer takes structured output from the LLM and renders it as live, interactive UI components in the chat. For example, the LLM might return a a Thesys DSL snippet (domain-specific language) snippet that describes a bar chart for campaign metrics. The GenUI frontend (like the C1 React SDK) reads that and actually displays a chart in the chat window that the user can hover over or even update.
• Rich Components: Common components include tables (for data lists), charts (bar, line, pie for trends and breakdowns), forms or input fields (if the agent needs the user to refine something or provide more info), buttons (to take quick actions or show options), and even images or videos. In a marketing agent scenario, imagine asking for an AI dashboard builder view – the agent could generate a mini dashboard UI on the spot.
• Interactivity and State: A real GenUI like C1 isn’t just a one-shot render; components can maintain state and allow further interaction. For instance, if the agent shows a table of top campaigns, you could have a dropdown (generated by the AI) to filter the table by region or channel. The user’s selection can be fed back to the AI agent as a follow-up message or function call to update the data. The result is a fluid, app-like experience delivered in a conversational format.
• Customization & Branding: The GenUI layer can be styled to your brand so it doesn’t look auto-generated. You can configure themes, colors, and component styles in frameworks like C1. So, even though the content is dynamically created, it adheres to your company’s UI guidelines – your marketing team sees a familiar look and feel (logos, brand colors, etc.) around those charts and buttons.

How to integrate C1 by Thesys

Integrating C1 by Thesys into your project upgrades your chatbot interface into a full AI agent UI with minimal effort:

• Point LLM API Calls to C1: Instead of calling an LLM API directly, you call the C1 API endpoint. For example, if you use OpenAI’s SDK, you just change the base URL to Thesys (and include your Thesys API key). The rest of the call (the model name, the messages) works the same. Now, when the AI responds, it can include special notations for UI components.
• Use the C1 Frontend SDK: In your web app (or wherever the chat UI lives), install the C1 React SDK. Replace your chat message renderer with C1’s component. This SDK will detect the Generative UI instructions in the AI’s response and automatically render real React components. So if the response includes a table definition, the SDK generates an actual HTML table in the chat. If it’s a chart, it uses a chart library under the hood to draw it.
• Configure Styling: Through the Thesys Management Console or in your code, you can set theme options so that generated components match your branding. For instance, you can specify your brand’s primary color, and all GenUI buttons and highlights will use that color. C1 ensures the dynamic UI doesn’t look out of place in your product.
• Prompt the AI for UI: Optionally, guide your LLM in prompts to produce UI output when helpful. For example, in the system prompt you might say: “You can answer with Generative UI components when it makes the answer clearer – e.g., use a <Chart> for data comparisons, <Table> for lists, <Form> to collect inputs.” The AI will then know it has this power. In practice, you might ask: “Show me our email vs. social lead generation this month,” and the agent (via C1) will return a comparison chart component rather than a long textual description.

The result is that with just a few lines of config, you’ve turned a plain chatbot into an adaptive marketing dashboardthat you can converse with. No need to manually code a chart or create a static template for every possible output – the AI + C1 handle it on the fly. For more details, see the C1 Quickstart in the Thesys Documentation and experiment in the Thesys Playground. Thesys also provides Demos you can check out for working examples of Generative UI in action.

Alternatives and docs

C1 by Thesys is a dedicated solution for Generative UI – it’s designed to plug into any AI stack and render components in real time. At the moment, there are few direct competitors to this approach. Most teams building AI products either hand-craft static UIs (like hardcoding a chart for a specific response) or use parsing libraries to extract data from the AI and then manually feed it into front-end components. Those methods can be brittle (the AI’s output format might change unexpectedly) and labor-intensive. GenUI with C1 avoids that by having a consistent structure (Thesys’s DSL) and handling the heavy lifting of UI rendering for you. In short, you focus on what the AI should show, and C1 figures out how to show it.

For more on the concept, the Thesys blog post “What is Generative UI?”thesys.devthesys.dev is a great resource. It explains how GenUI flips the traditional UI paradigm by letting the interface create itself dynamically for each user’s needs. In practice, embracing GenUI means your marketing AI agent isn’t limited to chat – it becomes a mini application that can adapt to each query, providing a much better AI UX (user experience for AI interactions) than plain text streams.

Best practices for marketing

• Use GenUI for Clarity: Identify where a visual or interactive element would make the answer easier to digest. Marketing data is a prime candidate – don’t have the agent list 20 metrics in a paragraph if it can show a nicely formatted table or highlight the top 3 in a bar graph. A/B test results, budget allocations, timeline schedules – these are great opportunities for GenUI.
• Keep it Simple: While GenUI can create complex layouts, ensure the agent doesn’t overload the user with too many elements at once. The UI should remain clean and focused. For instance, if asked for “monthly website traffic trend”, a single line chart is perfect. You don’t need three charts and two tables unless specifically requested. Guide the AI (via examples) towards one primary visualization at a time for each answer, with maybe a short text explanation alongside.
• Offer Next Steps: Interactive components can invite the user to continue the conversation fluidly. Use this to your advantage in marketing workflows. If the agent shows a chart of campaign ROI, maybe include a “Generate Recommendations” button below it (the AI can output a button component with that label). When clicked, it could trigger the agent to then provide suggestions based on the data. This checklist-like approach, enabled by UI, helps users explore insights without typing a new question every time.
• Test on Different Devices: If your marketing team might use the agent on mobile vs desktop, ensure the GenUI components are responsive or have fallbacks. C1’s components are generally responsive by design (being standard web elements), but double-check that a wide table or chart still looks okay on a narrow screen.
• Educate Users: Introduce the GenUI features to your users so they know it’s more than a chatbot. For example, a brief welcome message: “👋 I’m Marko! I can show you charts, tables, and more. Ask me something like ‘Show campaign reach by channel’ to see Generative UI in action.” This sets the expectation that the interface is rich, and users will start to take advantage of those capabilities.

Example for marketing

When I ask Marko, “Compare our email and Facebook campaign leads for September”, instead of a lengthy text, the AI (via C1) responds with a bar chart comparing email vs Facebook leads, and a one-liner: “Email brought 500 leads, while Facebook brought 300 leads in September.” Below the chart, there’s even a dropdown (generated by the AI) labeled “Select Month” – allowing me to change the month and instantly update the chart (the agent will fetch new data when I use the dropdown). This is Generative UI in action: my simple question turned into an interactive visual answer. Without me explicitly asking, the agent knew a chart would be more insightful than just words. I can literally see the difference in leads, and even engage with the data. It feels less like talking to a bot and more like using a smart, conversational marketing dashboard. This kind of adaptive AI interface is what makes an AI agent truly powerful for end-users.

6. Deployment, Monitoring, and Governance

What this layer is

This final layer is about getting your marketing AI agent out into the world (deployment), keeping an eye on it (monitoring), and ensuring it operates safely and reliably (governance). After you’ve built the agent with all the layers above, you need to integrate it into your product or workflow and then continuously oversee its performance and compliance. Essentially, it covers the DevOps and MLOps side of your AI agent.

Function

• Deployment: This covers where and how the agent runs for users. For instance, you might embed the agent into your website or internal tools. With solutions like C1 Embed (accessible via the Thesys Management Console), you can generate an embed code snippet to drop the agent widget into a page, much like embedding a chat support widget. Deployment also includes scaling considerations: ensuring the backend (LLM, vector DB, etc.) can handle the load as usage grows.
• Hosting Environment: If you’re not using a fully managed service, you might deploy the agent’s backend components on a cloud platform (AWS, Azure, GCP). But many will use a hybrid: e.g., LLM calls through API (no self-hosting needed for GPT-4 if using OpenAI’s cloud), the GenUI served via web app, etc. Thesys C1 being cloud-based simplifies this – your focus is mostly on the front-end embed and setting config in the console.
• Monitoring (Observability): Once live, you’ll want to track metrics like how accurate the agent’s answers are, how fast responses come, uptime of the service, and user satisfaction. Tools such as OpenTelemetry can instrument your app to log requests, response times, and errors. Prometheus (with a visualization tool like Grafana) might be set up to monitor system metrics (CPU/memory of any components you host, or API latency if you track it). Monitoring also includes application-specific logging – e.g., logging each query and answer for later review.
• Governance and Security: This involves controlling access (who can use the agent, which data it can see), protecting user data, and ensuring compliance with regulations. For instance, if the agent handles customer data, you’d follow security best practices like encryption and meet standards such as the OWASP Application Security Verification Standard (ASVS)owasp.org. Governance might also involve content moderation (making sure the agent isn’t producing disallowed content) and model version management (deciding when it’s safe to upgrade the AI model to a new version).

Alternatives

• Hosting/Embed Solutions: The easiest path for many is to use the Thesys Management Console to deploy – you get a snippet to embed the chat in your site, and Thesys handles hosting the needed runtime (the GenUI API calls, etc.). If going custom, you might deploy on Vercel or Netlify for front-end and use cloud functions or a backend service for any server logic.
• Observability Stacks: Besides OpenTelemetry/Prometheus, there are hosted services like Datadog or New Relicthat can monitor your application end-to-end (they might even have specific modules for ML ops). If using LangChain, their LangSmith platform also provides monitoring for LLM apps, capturing traces of how the agent is processing each request.
• Alerting: For governance, you might integrate alerts. E.g., use Prometheus Alertmanager or Datadog’s alerting to notify you if error rate spikes or if the AI’s response time crosses 5 seconds. This ensures you catch issues quickly (maybe the vector DB went down or an integration is failing).
• Security Reviews: Alternatives here are more guidelines: you could use OWASP’s checklist (like OWASP ASVS mentioned) or even hire an external firm to pentest your AI application if it’s high-stakes. For privacy, if dealing with personal data (say user emails, profiles), ensure compliance with GDPR or other relevant laws – which might involve adding features like data deletion on request, etc.

Best practices

• Gradual Rollout: If this agent will be used widely (say by customers), consider a beta rollout. Start with internal team users, gather feedback and ensure stability, then expand. This limits exposure if something goes wrong early on.
• Track Key Metrics: Define what success looks like and monitor it. For a marketing agent, one key metric could be accuracy rate (perhaps measured by a weekly manual review of a sample of answers). Others: average response time (keep it low so users are happy), usage rate (how many queries per day, indicating adoption), containment rate (did the agent handle queries without needing human help), and perhaps resolution time saved. If you can estimate that each query answered by the agent saved a marketer 5 minutes, you can start quantifying productivity gains.
• Regular Retraining/Updates: While the LLM itself you might not retrain (unless you fine-tune it occasionally on new data), you should update the knowledge base (Layer 3) and the guardrails (Layer 1) regularly based on what you observe. For example, if users keep asking for a certain report the agent can’t handle, you might add that data source or create a new function to fetch it. Or if the brand voice changes (new messaging guidelines), update the style instructions.
• Privacy Considerations: Make sure the agent isn’t storing sensitive info inadvertently. Thesys C1 does not store your data (it streams through, according to their security promises), but if you log conversations for monitoring, ensure those logs are protected. Mask any personally identifiable information in logs if not needed. An example: if the agent is internal, it might sometimes handle customer emails or names – perhaps hash or truncate those in any system logs for compliance.
• Failure Planning: Have a plan for when the agent is unavailable or malfunctioning. If the LLM API is down, your agent should respond with a polite apology and note that it’s currently unavailable, rather than hanging indefinitely. Perhaps even direct the user to a backup support channel if needed. Also, consider circuit breakers – if an integration fails repeatedly, maybe temporarily disable that function and notify your dev team.

Example for marketing

After rigorous testing, we deploy Marko on our internal marketing portal using the C1 Embed code. The team can now access it at a special URL and in our Slack (we integrated it as a Slack bot as well). We set up monitoring: every query and response is logged to a secure dashboard. In the first week, we watch those logs and see 100 questions asked, with an average response time of 3 seconds – not bad. We notice two instances where Marko gave a wrong figure (mixing up two campaign names). We log those as errors, correct some data labeling in the knowledge base, and that issue hasn’t recurred.

On the governance side, only authenticated employees can access the agent (it uses our SSO for login). We’ve restricted Marko’s access so it cannot pull customer personal data – even though it’s technically connected to CRM for aggregate stats, it has no function to fetch individual customer details. This was a conscious governance choice. We also briefed the marketing team: “Don’t paste customer PII into the agent chat,” and we added a snippet in the UI about that.

Finally, we put in place an uptime alert: if Marko hasn’t answered any query in an hour during work hours, an alert pings me to check on it – just in case something froze. So far, it’s been stable and the team is starting to depend on it for quick insights. As a result, our marketing stand-up meetings are shorter because folks come armed with data Marko provided in seconds, a task that used to take an analyst hours.

Benefits of a Marketing AI Agent

Implementing a marketing AI agent can bring significant advantages to your organization:

• Efficiency and Time Savings: Routine marketing tasks that once consumed hours can be automated and accelerated. Copywriting, generating reports, segmenting data – the agent handles these in secondsprofessional.dce.harvard.edu. This frees up your human marketers to focus on strategy and creativity rather than grunt work. The agent’s quick responses and actions mean campaigns get to market faster and opportunities are seized in real time.
• Consistency and Availability: Your AI agent is always on and always consistent. It doesn’t tire or vary from the brand guidelines. This ensures that whether it’s answering a product question or pulling metrics at 2 AM, it provides clear, reliable support for marketing workflows. Team members get the same quality of insight anytime, anywhere. An AI agent also scales effortlessly – it can chat with 5 or 50 people at once, unlike a single human specialist.
• Personalization and Contextual Awareness: Because you feed it your company’s data, the agent can give personalized answers rooted in your business context. Ask about your campaigns or customers, and it will tailor the response using that internal knowledge. Over time it can even learn preferences – for example, a marketing manager who always cares about ROI will get that metric highlighted first. It’s like an analyst that remembers what matters to you.
• Better Decision-Making: The agent can surface insights from large datasets that might be hard for a person to digest quickly. It can summarize trends from thousands of rows of data, or instantly compare performance across channels. By presenting information visually with tables or charts (thanks to the Generative UI, yielding rich LLM UI components), it helps marketers grasp the story in the data. This leads to more informed decisions – you’re less likely to overlook a key stat when the agent has already analyzed and displayed it prominently.
• Interactive Learning and Brainstorming: Beyond hard analytics, a marketing AI agent is great for brainstorming and “what-if” exploration. It’s an ever-patient partner for idea generation – you can ask it to suggest headline variations, refine messaging for different audiences, or even role-play as a customer to test campaign angles. The AI UX is conversational, so it feels natural to iterate on ideas. Plus, with GenUI elements like forms or sliders, you can tweak inputs (like target demographics or budget levels) and see the agent adjust its suggestions on the fly. This interactive capability can spur creativity and uncover angles the team hadn’t considered.

In essence, a marketing AI agent combines the analytical rigor of a data tool, the availability of a chatbot, and the adaptability of an interactive app. By leveraging a dynamic Agent UI and robust AI, it becomes an always-on marketing assistant that elevates your team’s productivity and outcomes.

Real-World Example: Marko the Marketing Assistant

Let’s bring it all together with a day-in-the-life scenario. Meet Marko, our AI marketing agent we’ve built using the steps above, now working alongside the marketing team:

Scenario: Jenna, the Head of Marketing, is preparing for a quarterly review meeting. She opens the marketing portal and greets Marko: “Hey Marko, what were our top 3 lead sources in Q3 and how many leads from each?” Marko is on it. In a second, it replies with a brief summary: “Our top lead sources in Q3 were 1) Organic Search – 8,500 leads, 2) Email Marketing – 6,200 leads, 3) Facebook Ads – 4,950 leads.” Alongside the text, Marko has displayed an interactive bar chart ranking the lead sources, using Generative UI (GenUI) to make the data pop out visually. Jenna can clearly see the drop-off after email marketing.

Alt: Interactive output from Marko – Marko, the marketing AI agent, responds with a bar chart showing top lead sources (Organic, Email, Facebook) for Q3, and an interactive dropdown to select different quarters. This Generative UI element allows the marketing team to quickly glean insights and explore data in the chat interface.

Jenna clicks a dropdown labeled “Quarter” that Marko provided under the chart, switching it to “Q4”. The chart and figures update instantly (Marko fetched Q4 data in the background): now Email Marketing is number one in leads. Impressed, Jenna asks, “Can you draft a quick summary of why email did better?” Marko then uses its knowledge base to recall the Q4 campaign notes and responds with a couple of bullet points: “- In Q4, we ran a targeted holiday email campaign that boosted engagement. - Our subscriber list grew 20% in late Q3, giving us a bigger audience in Q4. These factors led email to surpass search in lead generation.” The answer is concise and backed by the data Marko has.

Finally, Jenna says, “Great. Can you draft two slides worth of bullet points and stats that I can use for the meeting?” Marko obliges. It knows it can’t actually make slides, but it creates a formatted outline: “Slide 1: Q3 Lead Sources – 1) Organic Search: 8.5k leads (40%), 2) Email: 6.2k (29%)… Slide 2: Q4 Lead Sources – 1) Email: 7.1k leads (33%, up 15% QoQ), 2) Organic Search: 6.8k (32%)…” Jenna copies these bullet points, which are well-structured, straight into her presentation. A task that might have taken an analyst half a day – gathering data, making charts, writing insights – took Jenna 2 minutes of chatting with Marko. And she feels confident in the results, because she saw the data directly and knows it’s accurate.

This example showcases how a marketing AI agent with a rich UI turns a simple Q&A into actionable outputs. Marko not only answered questions, it visualized data and produced content ready for use. It truly acted as a co-pilot for Jenna, helping her make a data-driven point at the upcoming meeting, without having to wait on the analytics team or crunch spreadsheets herself. That’s the power of combining an LLM’s intelligence with Generative UI in a real-world marketing workflow.

Best Practices for Building a Marketing AI Agent

Creating a successful marketing AI agent involves more than just the tech stack – you also need sound design and governance principles. Here are some best practices to ensure your agent is effective and well-received:

• Keep the Agent UI Focused: Don’t overwhelm users with too many options or flashy elements. Simplicity is key for AI UX. Start with a straightforward chat interface. As you add Generative UI (GenUI) components, ensure they truly add value. Each chart or button should have a clear purpose. A clean, uncluttered Agent UI helps users trust and understand the agent’s outputs.
• Leverage Generative UI (GenUI) Wisely: Use GenUI to show, not just tell. When the agent’s answer involves data or multi-step input, present it with interactive components. For example, a marketer asks for weekly traffic – a line chart GenUI component will communicate the trend better than paragraphs. If the agent suggests actions (like improving SEO or launching a campaign), consider adding a button like “Implement SEO Tips” that, when clicked, breaks down the steps or triggers the next analysis. By moving beyond text, you create an AI-powered user interface that feels engaging and modern.
• Regularly Refresh and Curate Data: A marketing agent is only as good as the information it has. Update its knowledge sources on a regular schedule (e.g., load new analytics at month-end, add new product info before launches). Also, curate what it knows – more data isn’t always better if it includes irrelevant or outdated info. Keeping the content fresh and relevant ensures the agent’s answers remain accurate over time.
• Include Humans in the Loop for Critical Tasks: For high-stakes or sensitive marketing decisions, keep humans involved. The agent can draft content or make recommendations, but have a person review when it’s something critical like a major PR announcement or a big budget allocation. This might be as simple as programming the agent to say "I suggest A/B testing these two strategies. Would you like me to proceed or would you like to review the plan?* There’s immense value in AI speed, but human oversight provides assurance, especially early on.
• Monitor Performance and Feedback: Set up a feedback loop with your users (the marketing team). Encourage them to thumbs-up or down responses or send feedback when the agent is off. Track metrics like how often users need to rephrase questions or correct the agent. This can highlight areas for improvement – maybe the agent doesn’t understand certain marketing acronyms or tends to give too lengthy answers. Use this info to refine prompts, add training examples, or adjust the UI. Over time, this iterative approach will significantly boost the agent’s quality.
• Document Policies and Processes: Treat your marketing AI agent as part of the team – it should follow company policies too. Document how it should handle privacy (e.g., “never share customer personal info in answers”), compliance (e.g., include footnotes for financial figures if required by your compliance rules), and brand tone. Also document the development process: when you retrain or update data, note what changed. If an issue arises, this documentation helps troubleshoot and maintain consistency. Essentially, good governance documentation is part of making the AI a sustainable success.

Common Pitfalls to Avoid

Even with the best intentions, there are some common mistakes when building an AI agent. Steer clear of these pitfalls to ensure your marketing AI agent doesn’t run into trouble:

• Overloading the Interface: It can be tempting to cram the UI with every possible widget and stat (especially with GenUI making it easy), but this often backfires. Too many charts or buttons in one view can confuse users. Avoid the “kitchen sink” syndrome – each response or dashboard view generated by the agent should be focused. Remember, the goal is to simplify the user’s life, not present a wall of information. Start simple; you can always add more interactivity once you see a clear need.
• Relying on Stale or Untagged Data: If you don’t keep the knowledge base updated or if the data lacks context (like timestamps, region tags, etc.), the agent could give incorrect answers. For example, an old pricing sheet might lead it to quote the wrong price. Always ensure data is timestamped or versioned and that the agent knows what’s current. Implement a strategy to archive or label older info (e.g., mark anything from 2+ years ago as “archived” so the agent uses it only if explicitly asked for historical data). Using stale data in marketing – where trends change quickly – can be especially misleading.
• Skipping Guardrails and Validation: Neglecting the guardrail layer (Layer 1) or not validating user inputs can lead to embarrassing or risky outputs. Without rules, the agent might attempt to answer things it shouldn’t (like giving legal advice about marketing claims or making up data when uncertain). Always include basic guardrails for tone and safety, and validate critical actions. For example, if the agent has a budgetApproval(amount) function, ensure it doesn’t approve amounts beyond a threshold without extra confirmation. Not having these checks is like deploying an employee without any training – unpredictable and potentially damaging.
• Deploying “Write” Actions Without Safeguards: Allowing the agent to directly execute actions (sending emails, posting on social media) without any approval or review can be dangerous. One typo or misinterpretation and you’ve blasted a mistake to thousands. Always have an approval step or limit scope initially (maybe let it save a draft, not hit send immediately). As mentioned, human-in-the-loop is key for outward-facing communications until you’re extremely confident. Even then, periodic audits of what the agent is doing autonomously are wise.
• Ignoring User Training and Change Management: Lastly, a non-technical pitfall – assuming people will just know how to use the agent. If your marketing team isn’t introduced properly, they might misuse it or not use it at all. Avoid dumping the tool on them without onboarding. Take time to demo what it can do, and clarify its limitations (e.g., “Marko knows data from 2020 onward” or “Marko can’t predict future trends with certainty, those are just suggestions”). If users treat it like magic and over-trust it, or conversely if they’re too cautious to try it, you won’t get the value. Encourage questions and have a channel for feedback or support as they get used to this new AI assistant.

Being mindful of these pitfalls will save you headaches and help your marketing AI agent project deliver on its promise: making your marketing team smarter and more efficient, without unintended side effects.

FAQ: Building a Marketing AI Agent

Q1: Do I need to be a developer or AI expert to build a marketing AI agent?
A: No – you don’t have to write ML algorithms from scratch. Many tools make it accessible. Platforms like Thesys provide APIs and a Playground where you configure the agent rather than code it. You will involve some setup (connecting data or tweaking prompts), but it’s more about understanding your marketing needs than hardcore programming. A product manager or tech-savvy marketer can often spearhead the project. Of course, having a developer to hook up integrations or an IT buddy to assist with data connections can help speed things up.

Q2: How is a marketing AI agent different from a regular chatbot?
A: A regular chatbot might answer a few predefined FAQs or spit out generic info. A marketing AI agent is more powerful and context-aware. It’s connected to your marketing data and systems, so it can answer specific questions like “What was our email CTR last week?” – a generic bot wouldn’t know that. Plus, with an adaptive Agent UI, the marketing agent can show you interactive charts or let you trigger actions (like scheduling a post) right in the conversation. It’s like comparing a simple Q&A bot to a full marketing assistant that not only chats, but also understands your business and can get things done.

Q3: What kind of tasks can our marketing AI agent handle effectively?
A: Think of tasks that are data-driven, repetitive, or conversational in nature. Great examples: summarizing campaign results, generating copy ideas (emails, ad headlines), answering product questions using your docs, segmenting customers (e.g., “list top demographics for product A”), and providing how-to guidance (like steps to set up a campaign). It can even do calculations (ROI, growth rates) on the fly. What it might not do well is highly strategic decision-making or very creative branding work – those still benefit from human insight. But for day-to-day marketing tasks and quick analysis, an AI agent shines by providing instant support through a user-friendly AI interface.

Q4: Can the marketing AI agent integrate with our existing tools like HubSpot or Google Analytics?
A: Yes, integration is a key strength of a well-designed agent. Through the Actions & Integrations layer, you can connect APIs or use services like Zapier. For example, you can enable the agent to pull live metrics from Google Analytics or create a contact in HubSpot. With recent advances like OpenAI’s function calling, the agent can be “taught” how to use these tools when needed. You’ll likely start with read-only integrations (fetching data). As you grow confident, you can allow write actions – like drafting an email in Mailchimp or posting a tweet. Each integration makes the agent more useful, essentially turning it into a chatbot UI builder for your existing marketing stack.

Q5: How do we ensure the AI agent’s answers are accurate and brand-safe?
A: To ensure accuracy, you give the agent authoritative data (so it doesn’t guess) and set up guardrails. By hooking in your knowledge base and analytics, the agent uses real numbers and facts. We also implement checks – for instance, we might run test questions (via something like OpenAI Evals or manual review) regularly to see if it’s staying on track. For brand safety, we’ve defined rules in its prompts (e.g., maintain positive, respectful tone, no off-brand language). We also monitor its interactions initially. The good news is that because the agent works off your data and clear instructions, it usually stays reliable. And unlike a human, it won’t go off-script – it’s consistent with the guidelines it’s been given, yielding a trustworthy AI-powered user interface experience.

Q6: What if the agent doesn’t know an answer or makes a mistake?
A: It’s important to set the expectation that the AI agent, like any assistant, might not know everything. If asked something beyond its scope (say a very niche question with no data provided), a well-designed agent will admit it or ask for clarification. We program it to not bluff – for instance, it might say, “I don’t have that information yet, would you like me to gather it?” If it makes a mistake (perhaps misunderstanding a question), users can correct it or use the feedback mechanism. We continuously update the agent based on these learnings. Over time, it improves. Think of it as training a new employee: there’s a learning curve. But with each error caught and fixed, the agent gets smarter. Meanwhile, we keep critical decisions under human supervision to mitigate any impact from errors.

Q7: Is a marketing AI agent secure? Can we trust it with our data?
A: Security is a top priority. We use encryption and secure APIs for data transfer, and we host the agent in a vetted environment (Thesys and the LLM providers have robust security measures). Access controls are in place – only authorized team members use the agent, and it only accesses data it’s meant to. We follow best practices (like OWASP ASVS guidelines for web app security) to safeguard data. Importantly, the agent doesn’t store your company data on its own – it fetches what it needs when asked, and you maintain control over those sources. With these measures, using the AI agent is about as secure as any other enterprise software tool. We also recommend avoiding feeding it ultra-sensitive info just as a precaution (like you wouldn’t want any tool to have raw credit card numbers or passwords in a chat). In summary, with proper setup, a marketing AI agent can be trusted to handle business data responsibly, and we’ve designed ours with security in mind every step of the way.

Conclusion: Building a marketing AI agent may sound futuristic, but as we’ve outlined, it’s quite achievable with today’s technology. By combining a powerful LLM brain with your marketing data and a dynamic Generative UI (GenUI) front-end, you get an intuitive, adaptable AI agent UI. This agent can converse naturally while unveiling charts, forms, and answers tailored to your needs – a leap beyond static dashboards and one-size-fits-all chatbots. The result is a marketing team empowered to make faster decisions, experiment creatively, and offload drudgery to their new AI assistant.

As you embark on creating your own AI agent, remember to iterate, involve your team, and have fun with the process – you’re essentially training a new digital team member. And you don’t have to do it alone. We invite you to explore Thesys resources to kickstart your project. Check out the Thesys Website for more on our vision as “the Generative UI company.” See live examples of Generative UI in our Thesys Demos. When you’re ready, jump into the Thesys Management Console to connect your LLM and data, and use the Thesys Playground to prototype your agent. Detailed integration guides and API references are available in the Thesys Documentation. We’re excited to see what you build – here’s to the new era of AI-powered marketing, where your next intelligent agent is just 7 steps away!