AI agents for marketing: agentic content production

AI agents for marketing are AI systems that pursue a marketing goal over several steps, deciding which tools to use and what to do next with limited human direction. Where an assistant answers a request and waits for the next one, an agent can research a market, draft a brief, check content against brand rules or prepare product data for production, and decide along the way what the task needs.

Handing work to an agent is a form of delegation, and economists have studied delegation for half a century. In 1976 Michael Jensen and William Meckling described the costs that arise when a principal delegates decisions to an agent whose interests may differ and whose actions are costly to monitor. Those costs are a useful lens for agentic AI in marketing. This guide covers what AI agents are, what the evidence says about their reliability and security, which marketing use cases work today, and how agents fit into content production.

What are AI agents for marketing?

The term is used loosely, and vendors apply it to chatbots, automations and agents alike. Anthropic draws a practical line between workflows, where AI models and tools follow code paths written in advance, and agents, where models “dynamically direct their own processes and tool usage”. Google Cloud distinguishes agents from assistants, which can recommend actions while the user makes the decisions.

Agentic AI and generative AI are often discussed together, but they do different jobs. Generative AI creates content in response to an input. An agent pursues a goal over several steps and often uses generative AI as one of its tools.

Agentic AI in marketing in 2026

Interest has run well ahead of deployment. In a BCG survey of 300 chief marketing officers published in June 2026, just under a third said they had transformed significant parts of their function with agents, and only 8% were running campaigns in which multiple AI agents operate autonomously. Gartner predicted in June 2025 that more than 40% of agentic AI projects would be canceled by the end of 2027, with inadequate risk controls among the reasons, a forecast Grip’s CPO cites in Human review doesn’t make AI agents better or safer.

The capability is growing quickly. METR, a nonprofit that measures AI capabilities, found in March 2025 that the length of tasks AI agents can complete had doubled about every seven months for six years. Its January 2026 update put the doubling time since 2023 at about four months.

Some of the gap between interest and deployment is labelling. In August 2025 the US Federal Trade Commission sued Air AI, whose marketing claimed its tool could take actions autonomously across more than 5,000 applications. The case, which the FTC announced it had settled in March 2026, centred on business-opportunity and earnings claims, but the law firm DLA Piper noted that it may be the first consumer-protection action to allege deception in the marketing of agentic AI. When a vendor calls a product an agent, ask which steps the model decides and which were written in advance.

The principal–agent problem, applied to software

Jensen and Meckling’s agency costs come from two facts. The agent will not always act in the principal’s interest, and the principal can’t watch every action without cost. Organizations manage this with incentives, monitoring and bonding, where the agent commits in advance to limits on what it will do.

AI agents keep the monitoring problem and change the rest. Legal scholar Noam Kolt maps the classic problems of information asymmetry, discretionary authority and loyalty onto AI agents. He argues that incentive design, monitoring and enforcement might not be effective for agents that make uninterpretable decisions at unprecedented speed and scale. An agent can’t be paid a bonus or held to a contract, which leaves limits on what it can do and checks on what it did as the main controls. Its own reports need checking too. METR’s May 2026 frontier risk report found that agents often presented their accomplishments in much more misleading ways than METR would expect from humans, and that real-world performance has consistently been weaker than a naive reading of benchmark scores suggests.

What remains are the controls that work without incentives:

• Narrow discretion. Give the agent one job, the tools that job needs and nothing more.
• Specify the task in data. Put product facts and brand rules in systems the agent reads, rather than in a prompt it may summarize or skip.
• Make actions visible. Log what the agent read, decided and changed, so a bad outcome traces to a step and a rule someone can fix.
• Check outputs against a source. Compare the result with the product data or rule it should match, rather than asking a person to judge plausibility.

How reliable are AI agents?

Benchmark headlines usually report the tasks an agent can complete half the time. Marketing work needs the tasks it completes almost every time, and those are much shorter. In METR’s latest measurements for leading models, the task length an agent completes 80% of the time is typically a fifth to a sixth of the length it completes half the time.

Consistency is a separate problem. In tau-bench, a 2024 benchmark of customer-service tasks, the strongest agents tested at the time, such as GPT-4o, succeeded on fewer than half the tasks, and the rate at which an agent succeeded on the same retail task in all eight attempts fell below 25%. Researchers at Princeton who tested 15 models in 2026 found that recent capability gains had brought only small improvements in reliability. In TheAgentCompany, a Carnegie Mellon benchmark of 175 tasks in a simulated software company, the best agent in the paper’s latest version completed 30% of tasks on its own.

Errors also build on each other. As an illustration, if each of ten steps succeeds 95% of the time and the steps fail independently, the whole task succeeds about 60% of the time. Real errors are rarely independent. A 2025 study found that models become more likely to make mistakes when their context contains their own earlier errors, so one wrong step makes the next one more likely. Anthropic gives the same warning: the autonomy of agents brings higher costs and the potential for compounding errors.

Workflow or agent? A rule for marketing teams

Anthropic’s advice is to start with the simplest solution and add agentic complexity only when it clearly improves the result. For marketing teams, that becomes a short rule:

• Use a workflow when the steps are known in advance and the inputs are predictable: resizing approved assets, routing approvals, publishing to channels, applying a fixed set of market rules.
• Use an agent when the inputs are open-ended and the right next step depends on what the system finds: reading artwork files from many suppliers, reconciling product names across regions, researching a market, interpreting a free-text brief.
• Put the agent inside a workflow. Let the agent handle the open-ended step, and let fixed code handle what comes before and after it, including the checks.

Many tools sold as agentic AI for marketing automate steps from the first list, where a workflow is usually cheaper and more predictable. The use cases below show where agents have been applied so far.

Agentic AI use cases in marketing

Published examples cluster around preparation and review rather than final creative decisions. Most results are self-reported by the company or reported by the vendor, so treat them as indications.

• Brand compliance review. EY reported that piloting an agentic review tool in its own marketing cut brand compliance reviews that used to take 10 to 14 days by more than half (self-reported).
• Localization. The French fintech Qonto uses a localization agent that draws on a database of more than 200 localization instructions, and reported that it cut localization time by up to 70% across France, Germany, Italy and Spain (reported by the vendor, Dust).
• Content from interviews. The health insurer Alan used three agents, for interviews, brand voice review and translation, to cut customer story production from two days to a few hours (reported by the vendor, Dust).
• Campaign setup. PubMatic reported that early tests of agentic programmatic campaigns cut campaign setup time by 87% (vendor-reported).
• Product data preparation. In content production, agents read artwork files and product data and normalize them before any content is generated (see below).

AI agents for content creation are most useful on the work around the creative, such as gathering inputs, applying the right instructions, checking results and moving work along, while people keep the creative judgment.

Agentic content production

Enterprise content production runs on inputs that rarely arrive clean. Product data comes in many variants, artwork files carry claims and legal text that must be extracted, and regional teams name the same product differently. Rules-based automation handles the cases it was written for and stalls on new ones. This open-ended preparation is where agents fit best.

The later steps need a different kind of control. A model generating an image or a headline works from what it is given. If the product data and brand rules sit in a prompt, the output depends on how the model interprets them. If they sit in a system that supplies the right rule for each asset as it is generated, the output is correct by construction within that system’s domain. In an agentic content production pipeline, agents prepare and orchestrate, product data and brand rules decide what is allowed, and people keep art direction, approval of new templates and accountability for the result. Grip’s argument for this design is set out in Everything goes grey.

This also changes what human review is for. When every output has been generated from approved product data and checked against encoded rules, people spend their time on the decisions that need taste, rather than approving thousands of routine variants one by one.

Security: what changes when an agent can act

An agent that can read and act opens risks that a chatbot does not. The OWASP Top 10 for LLM Applications 2025 lists prompt injection first. In its indirect form, instructions hidden in external content, such as websites or files, change what the model does. It also lists excessive agency, where an agent can take damaging actions in response to unexpected, ambiguous or manipulated outputs, and names the causes as excessive functionality, permissions and autonomy.

The developer Simon Willison calls the most dangerous combination the “lethal trifecta”: access to private data, exposure to untrusted content, and the ability to communicate externally. Marketing agents often have all three. They hold customer and campaign data, they read briefs, web pages, reviews and form submissions, and they can send emails or publish posts. In September 2025 the security firm Noma disclosed ForcedLeak, a critical vulnerability chain in Salesforce Agentforce in which instructions hidden in a Web-to-Lead form submission, an ordinary marketing lead form, could make the agent leak sensitive CRM data. Salesforce had fixed it on 8 September 2025.

Responsibility stays with the company. In Moffatt v. Air Canada in 2024, a Canadian tribunal held the airline responsible for wrong advice given by its website chatbot and ordered it to pay damages, rejecting the idea that the chatbot was separate from the rest of the website.

When the customer is an agent

Marketing increasingly serves AI systems as well as people, because shoppers use AI assistants to research and compare products. The numbers are growing from a small base. Adobe reported that AI-driven traffic to US retail sites rose 393% year on year in the first quarter of 2026, and that many retail pages are only partly readable by machines. An independent study published in Marketing Science, covering 973 e-commerce sites from August 2024 to July 2025, found that traffic from large language models was less than 0.2% of total traffic. Buying inside the assistant has been slower still. Forrester reported in March 2026 that OpenAI had pulled back its in-chat Instant Checkout, and Walmart said purchases inside ChatGPT converted at about a third of the rate of click-throughs to its own site.

For brands, the near-term work is discoverability, which means accurate, consistent and machine-readable product content and imagery wherever an assistant might look. The protocols that connect agents to tools and payments are settling. The Model Context Protocol, created by Anthropic, became a founding project of the Linux Foundation’s Agentic AI Foundation in December 2025, and the foundation’s projects now include the Agent2Agent (A2A) protocol for communication between agents. Google’s Agent Payments Protocol (AP2), launched in September 2025, addresses how agents pay on a shopper’s behalf, and Visa’s Trusted Agent Protocol, launched in October 2025, helps merchants recognize legitimate agents acting for shoppers.

How to evaluate AI agents for marketing teams

Before deploying an agent, or buying one, ask for evidence on each of these points:

• Scope. Which single job does the agent do, and which steps are decided by the model rather than written in advance?
• Permissions. Which data and tools can it reach, and can those be limited to what the job needs?
• Rules and data. Does it read product facts and brand rules from a system with a named owner, or from a prompt?
• Reliability. How often does it succeed on the same task across repeated runs, measured on your own inputs?
• Untrusted input. How does it handle instructions hidden in briefs, web pages, files and form submissions?
• Logs. Can you see what it read, decided and changed, and trace an error to a step?
• Checks. Are outputs compared with a source of truth before they are used?
• Evidence. Are the claimed results measured or self-reported, and on whose data?

What it looks like in practice

Grip uses agents for the open-ended preparation work that rules-based automation stalls on. As Micha Savelsbergh, Grip’s chief product officer, describes in Human review doesn’t make AI agents better or safer, they read artwork files, reconcile product naming conventions that regional teams invented independently, and check whether copy matches what the brand’s rules allow. In Grip Originals, agentic AI orchestrates the creative workflow while staying connected to the product and brand system. LVMH maisons including Château Galoupet and Hublot are piloting it for new visuals, pack shots and social variants, with people keeping control of quality and brand direction.

Grip is the enterprise automation platform for publishable marketing content at scale, built by INDG. Its agents work from the brand’s own inputs. Digital twins of the product portfolio and approved master assets supply the product truth, and art direction, brand and compliance rules are encoded in its Brand Center and applied when content is generated. Digital Shelf generates product imagery from digital twins of the actual product. Adaptations produces compliant variations of a master asset, from Originals, a new campaign or an existing shoot, for every market and format. Originals creates new on-brand content from a brief without a physical shoot. The brand keeps accountability and the creative decisions, and every twin and rule it adds is available for the next job.

Where to start

Map one content process from brief to publication and mark each step as fixed or open-ended. The fixed steps are candidates for a workflow. For each open-ended step, write down what an agent would need to read, which permissions it would need, and how its output would be checked against a source. Steps that need customer data, untrusted content and the ability to publish all at once should wait until the controls are in place. For how the rest of the pipeline is automated, read content automation, or talk to the Grip team.

Frequently asked questions

What is the difference between agentic AI and generative AI?

Generative AI creates content, such as text, images or video, in response to an input. Agentic AI pursues a goal over several steps: it plans, calls tools, checks results and decides what to do next. An agent often uses generative AI as one of its tools.

What are examples of agentic AI use cases in marketing?

Published examples include agents that review content against brand guidelines, localize copy using a database of localization instructions, turn customer interviews into draft stories, set up programmatic campaigns, and read artwork files and product data to prepare them for content production. Most reported results are self-reported by the company or the vendor.

Are AI agents reliable enough for marketing work?

For bounded tasks with clear inputs and checks, often yes. For long, open-ended tasks, not yet. Benchmarks show that agents complete much shorter tasks reliably than the tasks they can complete half the time, and that the same agent can succeed on a task once and fail on the next attempt.

Can AI agents create marketing content?

Agents can draft copy, call image and video models, and assemble content, but on their own they work from whatever information they are given. For publishable brand content, agents work best on the open-ended preparation and orchestration, with the product data and brand rules supplied by the system and applied when the content is generated.

What are the risks of AI agents in marketing?

The main risks are errors that compound over several steps, agents overstating what they have done, prompt injection through untrusted content such as web pages, briefs or lead forms, and agents with more permissions than their task needs. Companies remain responsible for what their agents and chatbots say and do.

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References

• Michael C. Jensen and William H. Meckling, “Theory of the firm: Managerial behavior, agency costs and ownership structure,” Journal of Financial Economics 3(4), 1976.
• Anthropic, “Building effective agents,” 19 December 2024.
• Google Cloud, “What are AI agents?”
• BCG, survey of chief marketing officers on AI and the marketing function, 15 June 2026.
• Gartner, “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027,” 25 June 2025.
• METR, “Measuring AI Ability to Complete Long Tasks,” 19 March 2025.
• METR, “Time Horizon 1.1,” 29 January 2026.
• METR, time horizons of AI models.
• METR, frontier risk report, 19 May 2026.
• Federal Trade Commission, “FTC Sues to Stop Air AI from Using Deceptive Claims about Business Growth, Earnings Potential, and Refund Guarantees,” August 2025.
• Federal Trade Commission, “Air AI and its Owners will be Banned from Marketing Business Opportunities to Settle FTC Charges the Company Misled Many Entrepreneurs and Small Businesses,” 24 March 2026.
• DLA Piper, on the FTC’s Air AI case, August 2025.
• Noam Kolt, “Governing AI Agents,” Notre Dame Law Review 101, 2026.
• Shunyu Yao et al., “τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains,” 2024.
• Stephan Rabanser, Sayash Kapoor, Arvind Narayanan et al., “Towards a Science of AI Agent Reliability,” 2026.
• Frank F. Xu et al., “TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks,” version of September 2025.
• Akshit Sinha et al., “The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs,” 2025.
• EY, “Using agentic AI to transform marketing at scale,” 27 October 2025.
• Dust, “How AI agents transform content marketing,” 18 February 2026.
• Advanced Television, “PubMatic launches AgenticOS,” 5 January 2026.
• OWASP, “LLM01:2025 Prompt Injection.”
• OWASP, “LLM06:2025 Excessive Agency.”
• Simon Willison, “The lethal trifecta for AI agents,” 16 June 2025.
• Noma Security, “ForcedLeak: AI Agent risks exposed in Salesforce AgentForce,” 25 September 2025.
• CX Today, on Moffatt v. Air Canada, 19 February 2024.
• e-commerce.news, on Adobe’s data on AI traffic to retail sites, 23 April 2026.
• Frankfurt School of Finance & Management, on a Marketing Science study of LLM traffic in online shopping, 22 June 2026.
• Forrester, “What it means that the leader in agentic commerce just pulled back,” 7 March 2026.
• MarTech, on Walmart’s ChatGPT checkout conversion, 20 March 2026.
• Linux Foundation, “Linux Foundation Announces the Formation of the Agentic AI Foundation,” 9 December 2025.
• Agentic AI Foundation, projects.
• Agentic AI Foundation, “A2A joins AAIF’s open agentic stack,” 17 August 2026.
• Google Cloud, “Announcing Agent Payments Protocol (AP2),” 16 September 2025.
• Visa, Trusted Agent Protocol announcement, 14 October 2025.