Brands are using AI agents in advertising for twelve recurring jobs across five areas: planning and research, buying and negotiation, creative and versioning, measurement and reporting, and operations and compliance. In each job the agent does the legwork, while a person sets the goal and approves the commitments that carry money or reputation.

TL;DR. Most brand use so far runs inside existing platforms or through agencies, covering research, set-up, bidding, creative, reporting and policy fixes. Direct negotiation between buyer and seller agents is newer, built on shared standards such as IAB Tech Lab's AAMP and the Ad Context Protocol (AdCP). Across every example below, agents draft and act within limits, and people approve.

What do brands still decide when an agent does the work?

Agent autonomy in advertising works better as a ladder than a switch. One five-step version runs Observe, Recommend, Draft, Human-approved action and Bounded automatic action, with an agent moving up only as its record justifies it (the five stages of earned autonomy).

Brands have reason to hold the top steps tightly. In IAB's State of Data 2025 survey of 529 agencies, brands and publishers, 51% of brands said they were concerned about a lack of transparency into how agency and publisher partners use AI on their behalf. The same survey found 70% had yet to fully scale AI across planning, activation and analysis.

Deal-level approval is the clearest test of control. On the Alkimi Marketplace, for example, buyer and seller agents can negotiate, but people on each side keep approval rights over the deal. The agreed terms are written into a DealSheet at the moment of agreement, timestamped, held by both parties and retrievable by either side independently.

For named campaigns, see Examples of Brands and Agencies Using AI Agents in Advertising, and for how brand involvement is changing, see How Brands Are Using AI Agents in Their Advertising.

How do brands use AI agents for planning and research?

1. Consumer research and brainstorming

Agent: summarises research and drafts campaign angles from a brief.

Person decides: which consumer insight the campaign is built on, and whether any of the agent's angles fit the brand.

Example: at Mondelez's LUNA Bar, agents are limited to brainstorming and insights are developed by the marketing team (Digiday, 27 May 2026). "We are not handing it over to AI to help us develop that true consumer insight that would design the campaign," said Valerie Van Arkel, a brand director at Mondelez.

Risk: a confident summary can rest on thin sources, so check what the agent read.

2. Audience discovery

Agent: compares audience segments against the campaign goal and recommends a shortlist.

Person decides: which audiences the brand is willing to use, given consent, sensitivity and brand fit.

Example: Yahoo DSP lets partners' external agents, connected through APIs or the Model Context Protocol (MCP), analyse segment size and demographics and recommend audiences aligned to campaign goals (Yahoo, 6 January 2026).

Risk: a segment that fits on paper can still break the brand's own data rules.

3. Finding and comparing seller proposals

Agent: collects publisher proposals against a brief and ranks them, across more sellers than a planner could review by hand.

Person decides: which proposals go forward, and which sellers the brand will work with.

Example: IAB Tech Lab's AAMP 3.0 added OpenProposal, a specification for how sellers respond to buyer briefs in an agent-readable format (IAB Tech Lab, 22 September 2026). "OpenProposal fills a major gap between a buyer's brief and a buy commitment," said Anthony Katsur, CEO of IAB Tech Lab.

Risk: a vague brief produces a confident shortlist of the wrong things.

How do brands use AI agents for buying and negotiation?

4. Campaign set-up and trafficking

Agent: builds line items and applies targeting and settings a trader would otherwise configure by hand.

Person decides: the budget, the dates and sign-off before launch.

Example: Yahoo DSP reported that a trafficking agent built by Newton and the agency RPA, connected through MCP, had already executed programmatic guaranteed buys (Yahoo, 6 January 2026).

Risk: one wrong setting copied across every line item becomes an error in every line item.

5. Bidding and in-flight optimisation

Agent: adjusts bids and moves spend between line items while the campaign runs.

Person decides: the goal the agent optimises towards and the approval thresholds it cannot cross on its own.

Example: Duluth Trading Company's agency partner manages agentic bidding, while brand voice and humour stay under closer human oversight (Digiday, 12 May 2026). "We're comfortable with it because we know that our agency knows us very well," said Ellie Uberto, director of marketing at Duluth.

Risk: an agent chasing a cheap-reach target can quietly trade away quality.

6. Negotiating deal terms with seller agents

Agent: negotiates terms directly with a publisher's agent, using shared protocols so both sides read the same fields.

Person decides: whether the final deal is accepted, especially quality floors and make-goods.

Example: IAB Tech Lab's AAMP is designed to cover end-to-end ad decisioning and delivery workflows, and the Ad Context Protocol (AdCP), published by AgenticAdvertising.org, lets AI agents discover, plan, buy, sell and measure media.

Risk: a closed deal is not always a good one; in the Concourse Bench v1 benchmark, 95 of 110 signed contracts promised a viewability floor below 70%.

How do brands use AI agents for creative and versioning?

7. Creative concepts and storyboards

Agent: proposes concepts and taglines, then builds storyboards and finished video or display ads.

Person decides: which concept runs, the edits along the way, and approval before launch.

Example: Amazon Ads' agentic creative tool in Creative Studio takes an advertiser from product research to finished ads, explaining its reasoning at each step (Amazon Ads, 17 September 2025). "What truly sets this apart is how it explains its thought process as it's building my creative concept," said Dayexi Tomko, Brand Manager at Nestlé Health Science.

Risk: fluent creative can still be off-brand or make claims the brand cannot support.

8. Asset production and versioning at scale

Agent: produces variants for different markets, formats and placements from approved brand assets.

Person decides: the brand rules, the master assets, and which variants are fit to publish.

Example: L'Oréal's CreAItech, its in-house generative AI content platform, combines models from companies including Google, Adobe and NVIDIA and has been adopted by thousands of users (L'Oréal). L'Oréal states that it does not use AI-generated life-like faces, body, hair or skin to support or enhance product benefits.

Risk: generating variants faster than they can be reviewed creates a review problem, not a creative one.

How do brands use AI agents for measurement and reporting?

9. Performance insight and diagnosis

Agent: answers plain-language questions about results and suggests likely causes of a drop.

Person decides: whether the explanation holds up, and what to change.

Example: Integral Ad Science's IAS Agent surfaces trends across IAS dashboards and lets users customise, override or adopt its recommendations, each with an explanation (IAS, 16 December 2025).

Risk: a persuasive explanation can still be wrong, so treat it as a hypothesis.

10. Mix modelling and budget allocation

Agent: refreshes marketing mix models and forecasts the effect of moving budget.

Person decides: the budget, and how much weight a forecast gets against judgement.

Example: Hershey spent a year building a system of AI agents that delivers media modelling in weeks rather than months, which has helped steer decisions such as a lean into Reddit (Digiday, 14 May 2026). On agents buying direct from publishers, Hershey's Vinny Rinaldi said: "I still think there's human intuitiveness that's needed."

Risk: a model fed only the agent's own delivery data ends up marking its own homework.

How do brands use AI agents for operations and compliance?

11. Ad policy checks and disapprovals

Agent: explains why an ad was disapproved and prepares the fix.

Person decides: whether to accept the fix, and whether to appeal.

Example: Google's Ads Advisor, an agent in Google Ads powered by Gemini models, can find the root cause of a disapproval and in some cases make a fix, such as editing an ad URL, for the advertiser to approve (Google, 12 November 2025).

Risk: passing a platform's policy check is not the same as meeting the brand's own standards.

12. Agent identity and deal records

Agent: checks which agent is on the other side and logs what was agreed.

Person decides: which counterparties the brand will trade with, and who reviews the record.

Example: the IAB Tech Lab Agent Registry, part of AAMP, sets out a framework for agent identity, verification and disclosure "so participants can confidently understand who (or what) they are transacting with" (IAB Tech Lab).

Risk: if each side keeps its own log, the records can disagree, so agree in advance which one settles a dispute.

What should a brand test before handing an agent a budget?

Across all twelve jobs, agents draft and do, and people keep the decisions that commit money or speak for the brand. Before an agent moves up the autonomy ladder, it should be tested against counterparty agents where no real money is at stake.

That is the purpose of the Alkimi Gym, available now, where buyers and sellers build their own agents, set the rules they work to and spar against counterparty agents before any real money moves. Concourse, a live benchmarking product for AI media-buying agents, measures how they perform (contact).

Frequently asked questions

How are brands using AI agents in their advertising?

Mostly for research, campaign set-up, bidding, creative production, reporting and policy fixes, usually inside existing platforms or through agencies. Direct negotiation between buyer and seller agents is newer and is being built on shared standards such as IAB Tech Lab's AAMP and AdCP.

Do brands let AI agents run campaigns without human approval?

Rarely for decisions that commit money or shape the brand's voice. Bayer's Glenniss Richards said AI is "making us quicker, faster, certainly more agile" but "it's not owning or controlling our campaigns" (Digiday).

What is the biggest risk when an agent buys media for a brand?

An agent can complete a deal that meets the letter of the brief but leaves the brand weakly protected, for example on viewability floors. That is why deal-level approval and a shared record of what was agreed matter as much as the agent's speed.

12 Ways Brands Use AI Agents in Advertising, by Job