Agent Protocol

The AI agent ecosystem has a fragmentation problem: an agent built with AutoGen cannot natively call an agent built with CrewAI, and neither can be easily monitored with generic infrastructure tooling. Agent Protocol, maintained by the AI Engineer Foundation, solves this by defining a minimal, framework-agnostic REST API that any agent can implement — exposing endpoints for task creation, step tracking, and artifact retrieval.

The protocol defines three core concepts: **Tasks** (the work assigned to an agent, with inputs and a final output), **Steps** (the individual reasoning and action steps the agent takes to complete a task, each with its own input, output, and status), and **Artifacts** (files or data produced as part of task execution). Any infrastructure tool — monitoring dashboards, orchestrators, test runners — that speaks Agent Protocol can instrument any compliant agent, regardless of its internal implementation.

The enterprise value is greatest in multi-agent architectures. When a high-level orchestrator agent needs to delegate subtasks to specialist agents (a research agent, a code agent, a data analysis agent), Agent Protocol gives them a common language. It also transforms observability: because every step is a structured API response, you get full execution traces for every agent task without custom logging code. Compliance teams can audit exactly what each agent did, in what order, and with what results.