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In IAB Tech Lab’s Buyer Agent reference architecture, L1 sets portfolio strategy, L2 specialists handle individual channels, and L3 agents perform defined tasks such as research or execution. These labels describe where work sits in that architecture—not a universal scale of how independently an AI can act. To assess real-world autonomy, check what the system is permitted to do, what requires approval, and how its actions are monitored.
What do L1, L2 and L3 mean in the IAB Tech Lab architecture?
The IAB Tech Lab Buyer Agent reference design divides campaign work by scope: strategic coordination, channel expertise, and functional task execution. This structure is intended to narrow each agent’s job and let channel specialists work in parallel. It does not mean that every advertising platform uses the labels the same way, or that a lower-numbered or higher-numbered agent necessarily has more spending authority.
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L1: Portfolio Manager
The L1 agent receives the campaign brief, interprets its objectives and constraints, and allocates budget across channels. It gives channel specialists guidance and monitors whether their work fits the overall campaign strategy.
L2: Channel specialists
L2 agents take the allocation and direction from L1 and handle channel-specific work. The reference architecture illustrates specialists for areas including branding, connected TV (CTV), mobile apps, performance, linear TV, and deals. They can coordinate L3 agents for research, planning, and execution within their channel.
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L3: Functional agents
L3 agents are organized around specific functions rather than a universal “third level of autonomy.” Examples in the reference architecture include audience planning, inventory research, order or line execution, and reporting. The illustrated reporting agent is marked “Coming Soon,” so the diagram should not be read as evidence that every component is a production-ready feature.
Does Level 2 mean the same thing across the industry?
No. The IAB Tech Lab hierarchy is one reference architecture, not an industry-wide autonomy scale. IAB Europe’s survey, for example, uses Level 2 to describe people and agents jointly planning, delegating, and executing. That differs from the IAB Tech Lab use of L2 for channel-specialist agents. Always identify the framework behind a level label before comparing systems or interpreting a vendor’s autonomy claim.
A more useful question is what the system can actually do. A media-buying agent might be limited to read-only analysis, generate recommendations, prepare changes for approval, execute within preset bounds, or act without prior human approval. These are practical comparison categories, not formal levels defined by the IAB Tech Lab architecture.
What are advertisers using or considering agents for?
The IAB’s 2026 Outlook Study asked 161 respondents who were aware of agentic AI for ad buying or campaign execution how likely they or their companies were to use it for different tasks. The figures below combine respondents already using an agent for a task with those likely to use one. They measure reported use or intent—not audited adoption, market-wide penetration, or campaign performance.
| Task | Reported use or likely use |
|---|---|
| Performance analysis and outcome insights | 93% |
| Creative testing, selection, or optimization | 91% |
| Media planning and buying recommendations | 84% |
| Media pre-planning | 82% |
| Budget allocation, pacing, and optimization | 82% |
| Campaign decisioning and troubleshooting | 79% |
| Inventory discovery and evaluation | 75% |
| Programmatic deal execution and negotiations | 57% |
| Direct insertion-order (I/O) deal execution and negotiations | 45% |
The survey indicates stronger reported interest in analysis, creative optimization, and planning than in executing or negotiating deals—particularly direct I/O transactions. It does not establish that agents perform these tasks reliably or deliver a particular result.
How should you evaluate an agent’s autonomy?
Compare systems on the same operational criteria, rather than relying on an L1/L2/L3 label. Ask for specific answers and documentation, not just a description of what the agent is designed to do.
- Scope: Which campaign tasks and channels can it handle?
- Authority: Can it read data, make recommendations, draft or edit campaigns, launch them, move budget, negotiate, or commit a transaction?
- Approval gates: Which actions need human review? Can policy require approval for high-impact changes?
- Limits and reversibility: What restrictions apply to budget, targeting, inventory, and privacy? Can an action be undone?
- Observability: Are the inputs, recommendations, approvals, actions, and outcomes logged and available for review?
- Evidence and availability: Is the capability a demonstration, reference implementation, live production feature, or planned rollout—and in which geography?
Distinguish a planning recommendation from a live campaign change, and a proposed transaction from a committed one. A system may handle several steps in a workflow while still requiring a person to approve the consequential action.
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What governance is appropriate when an agent can change campaigns?
Governance should match the agent’s authority and potential impact. The Network Advertising Initiative’s voluntary guidance addresses AI use-case inventories, audience and segment review, testing and monitoring, disclosures, permissions and constraints, choice and signal handling, oversight and logging, contracting and risk allocation, and accountability. It recommends stronger safeguards as system complexity, capability, and autonomy increase, while keeping people accountable for results.
In practice, an agent with read-only access has a different risk profile from one that can alter targeting, spend, or privacy-related outcomes. Set permissions accordingly, test the system before broader use, monitor what it does, retain a reviewable record, and provide a way for people to intervene. Those controls are more informative than the agent’s level label alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How mature are agentic media buying and the standards work?
Survey responses suggest interest, but should not be mistaken for proof that autonomous trading is routine. In IAB Europe’s 2026 study, 58% of respondents expected agentic ad buying to reach operational use or scale within the next year. That is a reported expectation, not a guaranteed forecast. The study also found that 86% of respondents from organizations with 501 or more staff, compared with 48% from organizations with up to 500 staff, reported their most advanced production agentic system at Level 2 or above under IAB Europe’s definitions. Those self-reported levels are not directly comparable with IAB Tech Lab’s L2 channel-specialist role. In the same IAB Europe study, 78% reported a formal owner for AI governance, while 48% said they had AI guidelines specifically for advertising and marketing.
IAB Tech Lab’s Agentic Advertising Management Protocol (AAMP) is an umbrella initiative for agentic advertising standards. Its current page describes AAMP 3.0 as extending the workflow to the request-for-proposal or brief stage through OpenProposal, which supports campaign discovery and planning. It describes AAMP 2.3 as targeting enterprise deployment, interoperability, governance and trust, and transaction-ready audiences.
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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →IAB Tech Lab also links open-source buyer and seller agent SDKs. Its demonstration shows a workflow that creates a media plan from a brief, negotiates with a seller, confirms a transaction, and sends it to Google Ad Manager. That is a standards demonstration or reference workflow; it is not evidence that all commercial agents can carry out those steps in production.
What do current platform examples show?
Amazon Ads Agent: United States announcement
In its September 29, 2026 announcement, Amazon said advertisers could use manual tools, AI optimization, or a combination. Its Full-Funnel Campaigns ask advertisers for a budget, products, and creative, then use Amazon AI to plan, execute, and continuously optimize campaigns. Amazon said the feature was available to all U.S. advertisers at the time of the announcement.
The same announcement said DVA+ would begin rolling out in late October 2026; that rollout was still prospective as of October 5, 2026. Amazon also described conversational media planning, audience-targeting recommendations, natural-language analytics, and one-click application of sponsored-ad recommendations, with those conversational capabilities rolling out over the coming months.
Amazon reported that advertisers using natural-language targeting recommendations saw, on average, more than 25% additional unique customers and more than 10% lower cost per impression. These are vendor-reported results; the announcement does not provide enough methodological detail to generalize them to other advertisers or campaigns.
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A January 6, 2026 Yahoo announcement, as surfaced in search, described live activation, troubleshooting, and audience discovery capabilities, with optimization, quality-assurance, and measurement agents planned to roll out through 2026. The announcement’s exact current status is not established here, so treat this as a dated description rather than confirmation that those features are available now.
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