TripleLift proposes agentic AI advertising workflows to automate programmatic dealmaking

The shift toward agentic AI advertising workflows represents a move from simple automation to autonomous coordination within the ad tech stack. By leveraging the Model Context Protocol, platforms can reduce the friction inherent in multi-party programmatic deals, allowing human teams to focus on high-level strategy rather than technical execution. For the streaming ecosystem, this could accelerate the deployment of complex, data-driven campaigns across fragmented inventory. As these protocols mature, the industry should monitor the IAB Tech Lab for new standards that define how buyer and seller agents authenticate and transact without constant human intervention.

The push toward agentic AI in advertising is gaining traction across the programmatic ecosystem, with standards bodies and major platforms exploring how autonomous agents can coordinate transactions. The IAB Tech Lab has been working on specifications for agent-to-agent communication in digital advertising, building on its existing OpenRTB and ads.txt frameworks. In parallel, Anthropic's Model Context Protocol, which TripleLift references as the coordination layer for its proposed buyer and seller agents, has seen rapid adoption across software development tools. Anthropic released MCP as an open standard in late 2024, and by mid-2025 the protocol had been adopted by major platforms including OpenAI, Google DeepMind, and Microsoft, signaling broad industry consensus that a shared protocol is needed for agent interoperability.

On the business side, ad tech companies are racing to embed agentic capabilities into their platforms before standards solidify. Publicis Groupe announced in June 2026 that it would deploy AI agents across its media buying operations, targeting a 30% reduction in campaign setup time by automating insertion orders, creative trafficking, and reporting workflows. Meanwhile, The Trade Desk expanded its Kokai platform in early 2026 with AI-driven bidding agents that autonomously adjust campaign parameters based on real-time performance signals, representing one of the first production deployments of agentic logic in demand-side platforms. These moves suggest that TripleLift's proposal sits within a broader competitive dynamic where supply-side platforms are trying to match the automation sophistication already appearing on the buy side.

Technical benchmarks for agentic advertising remain limited, but early data from adjacent deployments offers useful reference points. Nokia and Google Cloud demonstrated at DTW Ignite 2026 that agentic AI systems could reduce network problem-solving times by 50% to 80% when specialized agents handled triage, anomaly detection, and remediation recommendations. While that deployment targeted telecom operations rather than ad tech, the architecture of specialized agents coordinating through a central orchestration layer mirrors the framework TripleLift proposes for buyer and seller agents. Verizon disclosed in June 2026 that its 60,000-site network now applies agentic AI to configuration changes and service assurance, while publicly calling for industry-wide interoperability standards for agentic systems. That same interoperability gap exists in advertising, where no standardized protocol yet governs how authenticate, negotiate, and settle transactions across competing platforms.