ARTF for Intelligence and Infrastructure Partners

This page summarizes how the Agentic RTB Framework (ARTF) affects specialist technology partners, including identity vendors, fraud and quality services, contextual and audience data providers, optimization services, and measurement partners. These entities remain independent businesses; ARTF defines a standardized deployment and interaction model that can make their integrations with many hosts more consistent and reliable.

graph LR Partner[Technology partner] --> Agent1[ARTF Bidding Agent] Partner --> Agent2[ARTF Bidding Agent] Agent1 --> Host[ARTF Host Platform] Agent2 --> Host

Audience

  • Identity and authentication providers
  • Fraud and quality verification services
  • Contextual and audience data providers
  • Optimization and bidding services
  • Measurement and attribution partners

1. Packaging services as agents

In the ARTF model, a technology partner delivers its functionality as a containerized agent that runs within host platforms. The agent encapsulates:

  • The provider's core logic or models.
  • Any required runtime dependencies.
  • A manifest describing capabilities, intents, and resource needs.

This allows a provider to focus on the quality and performance of its service, rather than on per-partner integration details.

2. "Package once, deploy broadly"

Instead of maintaining separate integration paths for each SSP, DSP, or exchange, ARTF enables a single packaging approach for partners:

  • Agents are built to conform to the ARTF specifications for containers and manifests.
  • Any host platform that supports ARTF can, in principle, deploy the same image and interact with it via the standardized API.

This can reduce:

  • Engineering overhead associated with bespoke integrations.
  • Time to market for new partnerships.
  • The complexity of ongoing maintenance across many different host environments.

3. Expressing capabilities with intents

ARTF uses intents to describe what an agent does and why it proposes particular mutations to the bidstream. For technology partners, this means:

  • Capabilities can be declared in a structured way (for example, identity resolution, fraud detection, enrichment, deal activation).
  • Hosts can more easily understand where and how to use the agent in their workflows.
  • Policy decisions about which agents to run and in what sequence can be applied consistently.

A clear mapping between an agent's capabilities and its declared intents helps ensure that it is invoked appropriately and that its outputs are interpreted correctly.

4. Data access and privacy

Because ARTF keeps the host in control of data access, agents do not receive unrestricted access to bid requests. Instead:

  • Hosts share only the specific fields or derived signals needed for the agent's declared intents.
  • Agents propose changes via OpenRTB Patch mutations, which the host can accept or reject.

For partners, this model:

  • Clarifies what data can be relied upon inside the agent.
  • Reduces the potential for accidental over-exposure of user or contextual data.
  • Supports collaboration with hosts and data owners who have strict privacy and compliance requirements.

5. Performance expectations

Running as a co-located container changes some aspects of performance planning:

  • Agents must operate within the host's time budget for each call, which is typically tight in real-time bidding contexts.
  • Efficiency and predictability are important; hosts may apply policies that limit or reject agents that exceed latency or resource thresholds.

Technology partners should therefore:

  • Optimize their logic and models for low-latency execution.
  • Monitor performance and error rates across host deployments.
  • Be prepared to update and tune agent containers as hosts evolve their infrastructure.

6. Strategic implications

ARTF can alter how technology partners position themselves in the ecosystem:

  • Greater comparability. Standardized deployment may make it easier for hosts and buyers to compare services on quality and effectiveness.
  • Specialization opportunities. Lower integration friction can support more specialized, narrowly focused services that can still be widely deployed.
  • Closer alignment with host and buyer needs. With agents running inside host environments, partners can design offerings that integrate more deeply with host workflows and buyer strategies, while still respecting data governance requirements.
  • Protection of proprietary logic. Partners continue to encapsulate models and algorithms inside their own containers; ARTF does not require disclosure of internal methods or pricing.
  • Neutral with respect to business models. The framework defines how agents run and interact, not how contracts, compensation, or commercial priority are arranged between parties.

Partners that prepare for ARTF by investing in containerization, manifest design, and performance optimization will be better positioned to support host and buyer needs as adoption increases, without undermining their existing roles in the ecosystem.