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The Gemini Interactions API is Google's unified API for calling Gemini models and specialized agents. It maintains server-side interaction state through interaction IDs and the `previous_interaction_id` parameter, while also supporting stateless requests. Each Interaction resource contains a chronological sequence of execution steps, including model thoughts, tool calls and results, and final output. The API supports text and multimodal generation, structured and strongly typed outputs, built-in and custom tools, tool orchestration, observable execution steps, background execution for long-running tasks, and chained image, video, and audio generation using shared context. Google documents it as generally available and recommends it for new Gemini API projects.

Context is Automattic's internal MCP server for rapidly adding tooling sets with governance and observability layers. It is used in the company's AI-enablement programs, which cover MCPs, shared skills, hooks, and autonomous agents.

Gemini Enterprise Agent Platform, formerly Vertex AI, is a Google Cloud platform for developers to build, run, scale, govern, and optimize autonomous AI agents and agent workflows. It provides session and memory management, persistence, parallel execution, model swapping, and built-in support capabilities.

The Context Graph API is Postman’s private, authenticated knowledge graph for giving coding agents a machine-readable map of an organization’s API ecosystem. It connects services, endpoints, schemas, consumers, owners, deployments, environments, telemetry, and runtime call relationships, resolving data from connected sources into a traversable graph. The graph is refreshed nightly and can expose dependencies beyond the repositories checked out locally, including observed runtime relationships and likely change blast radius. Agents query the graph through the asynchronous `/asks` endpoint with a natural-language question. A request returns a job ID, which the caller polls until completion; the result contains a written answer and structured data that the agent can use to investigate affected repositories, services, teams, or other resources before changing code. Postman describes it as a context layer that can be used with an existing model, IDE, or coding agent rather than requiring a replacement stack.

Google's Agents API defines and packages reusable AI agents with a base model, instructions, and a preconfigured environment. Agents run in managed cloud sandboxes rather than on the user's laptop; the environments can be shared with users or teams and accessed through an API call.