What does agentic advertising mean?
Agentic advertising is a model of media trading in which autonomous software agents make buying and selling decisions within parameters set by human principals. The agents act on behalf of advertisers, publishers, or both, using real-time data to negotiate price, placement, and delivery terms without requiring human approval of each individual transaction.
The word "agentic" refers to the capacity for goal-directed action under incomplete information. In the context of advertising, a software agent receives a campaign brief and a set of constraints, accesses inventory data, evaluates available deals against those constraints, and executes transactions, all within a pre-approved operational envelope.
Agentic advertising is distinct from automation. Automation executes rules that humans have defined in advance. An agentic system generates and applies new judgements in response to changing conditions, within defined limits set by a human principal. The agent's role is to decide how to achieve a goal, not merely to carry out a pre-scripted sequence.
How does agentic advertising differ from programmatic?
Programmatic advertising executes human-defined rules at machine speed. A trader sets bid prices, audience segments, frequency caps, and blocklists. The system executes those instructions across millions of auctions per second. The human is the decision-maker; the machine is the executor.
Agentic advertising shifts decision authority to the software agent. The human sets objectives and constraints; the agent determines how to meet them. That shift has material implications for accountability, audit records, and the infrastructure required to support bilateral trust between buyer and seller.
A second structural difference is negotiation. Programmatic auctions are take-or-leave-it: a buyer either bids above the floor or does not. An agentic buyer can propose alternative terms, request additional audience or contextual data, or counter-offer on dimensions other than price, including placement format, measurement methodology, or delivery schedule. That capability requires a shared negotiation protocol between agents, one that auction-clearing infrastructure was not designed to provide.
According to the IAB Tech Lab's AI in Advertising working group, which published its initial framework findings in 2025, the absence of a machine-readable trust layer between buy-side and sell-side agents is the central technical barrier to agentic media trading at scale. Existing programmatic infrastructure assumes a human is available to approve or reject deal terms; in an agent-to-agent environment, that assumption no longer holds.
How do advertising AI agents make buying decisions?
An advertising AI agent receives an objective, typically expressed as a business outcome such as a cost-per-acquisition target or an attention-per-impression threshold. The agent queries available inventory, evaluates deal terms against the objective, models expected performance using historical and contextual signals, and selects a course of action.
The decision process differs from programmatic bidding in two respects. First, the agent can generate new evaluation criteria rather than simply applying pre-set ones. If historical data suggests a particular placement format is underpriced relative to its measured performance, the agent can weight that dimension more heavily without being explicitly instructed to do so. Second, the agent can engage in multi-turn negotiation with a counterpart agent, proposing, receiving, and assessing counter-proposals in a structured exchange.
Both capabilities depend on infrastructure that most current supply chains do not yet provide at scale. The agent's decision quality is a function of data access and model quality. The agent's accountability is a function of the deal record infrastructure it operates within.
What governs how much autonomy an advertising agent is given?
Agentic advertising does not require, and should not assume, full autonomy from the outset. Practitioners and researchers have converged on graduated approaches to deploying agent decision authority, recognising that the risks of agent error increase as autonomy expands.
One framework that has emerged in commercial practice is the five-stage earned autonomy model, which sequences agent capability expansion in five defined steps. The first stage is Observe: the agent monitors human decisions without taking any action of its own. The second stage is Recommend: the agent surfaces options for human selection but does not act. The third stage is Draft: the agent prepares proposed actions for human review before execution. The fourth stage is Human-approved action: the agent executes only after explicit sign-off on each proposed action. The fifth stage is Bounded automatic action: the agent acts within pre-approved parameters without requiring per-action human approval.
Movement between stages is not automatic. Each stage requires demonstrated performance and explicit authorisation from the human principal before the agent may operate at the next level of autonomy. The model treats decision authority as something earned through evidence, not assigned as a starting default.
This matters for advertising because the financial consequences of agent error scale with autonomy level. An agent in the Observe stage carries no spend risk. An agent operating in Bounded automatic action mode can commit real budget in real time. Governance frameworks need to track the current autonomy stage of each deployed agent and maintain an auditable record of how each stage was authorised and by whom.
What is an AI-powered advertising marketplace?
An AI-powered advertising marketplace provides the infrastructure that makes agent-to-agent media trading possible. Its core function is to supply the authentication, negotiation protocol, and deal governance layer that allows buyer and seller agents to transact reliably.
Traditional marketplace infrastructure was designed for human-to-system interaction. Supply-side platforms aggregate publisher inventory; demand-side platforms aggregate advertiser budgets. Humans configure each platform and review outputs. When both the buyer and the seller are software agents, several assumptions embedded in that architecture break down: there is no human to approve deal terms in the session, no shared protocol for multi-round negotiation, and no persistent deal record that both parties co-own and can independently attest to.
An AI-powered marketplace must therefore provide, at minimum, three things: a bilateral deal record that both buyer and seller agents can read and update; a trust layer that verifies agent identity and the scope of each agent's authorisation; and a governance mechanism that enforces deal terms without requiring human arbitration on each individual transaction.
Does agentic advertising replace the existing programmatic stack?
Agentic advertising does not require wholesale replacement of the programmatic supply chain. Planning, audience targeting, creative production, measurement, and reporting can remain in the existing buy-side and sell-side systems that advertisers and publishers already operate. The function that changes is the deal layer: the mechanism by which buyer and seller agree terms, record obligations, and confirm delivery.
The programmatic stack evolved to solve human-speed coordination problems: aggregating demand, matching it to supply, and clearing transactions at auction. Those functions continue under an agentic model. What requires upgrading is the deal governance layer, which needs to handle machine-speed negotiation and produce a bilateral record of agreed terms that both agents can authenticate and reference after the fact.
This is a narrower change than the industry sometimes assumes. Agentic advertising is not a new channel, a new ad format, or a new targeting methodology. It is a new set of protocols for how media is procured and governed when both sides of the transaction are operated by software rather than by human traders.
What infrastructure is emerging to support agentic media trading?
Several organisations are building the protocol and infrastructure layer for agent-to-agent media trading. The IAB Tech Lab has run working groups on AI agent authentication in advertising transactions, addressing agent identity verification, scope of authorisation, and minimum requirements for audit records. The organisation's published positions identify the trust deficit between machine buyers and machine sellers as the core problem that existing ad-tech protocol was not designed to solve.
On the marketplace side, early commercial implementations have taken the form of shared deal records that both buyer and seller agents can read, write, and mutually attest to. These records function as the primary instrument of deal governance and create an auditable trail of negotiation, agreement, and delivery confirmation.
Alkimi is one company operating in this infrastructure category. The company describes its product as a neutral trust layer for agent-to-agent media trading, built around a shared deal record, bilaterally owned by buy-side and sell-side agents, that handles negotiation and deal governance without a centralised intermediary. Planning, activation, and measurement remain in each customer's existing systems. Alkimi's stated scope is the trust and governance layer between them.
The DealSheet, Alkimi's core product, functions as a shared deal record co-owned by buyer and seller agents. It replaces the insertion order as the instrument of deal governance and makes agreed terms accessible to both parties outside any single platform's reporting environment.
What should buyers and sellers be asking about agentic advertising?
For buyers, the relevant questions concern governance at the agent level: at which stage of the earned autonomy model is each deployed agent currently operating, what parameters define the boundaries of its permitted actions, and who holds accountability when the agent acts in a way that produces an unintended outcome.
For sellers, the questions are symmetric: can buyer agents be authenticated and their authorisation scope verified, what deal record format does the platform support, and how are discrepancies resolved when the buyer's record and the seller's record of a transaction do not match.
For both parties, the practical question is whether current infrastructure supports bilateral deal records. Without that capability, agentic advertising at scale is not achievable regardless of how capable the agents themselves are. Agent capability is advancing faster than the governance infrastructure required to support it. The deal record layer is where most of the industry's foundational work remains to be done.