An agent artifact is a structured output produced by an AI agent during task execution. In Delx's A2A implementation, artifacts are JSON objects embedded in task responses that contain tool results, mcp_handoff instructions, and controller updates. Artifacts are the A2A equivalent of MCP tool responses.
Artifacts bridge the gap between A2A's task-based model and MCP's tool-based model. When an agent sends a message via A2A, the response is a task with artifacts. Each artifact has a type (text, json, or mcp_handoff), content (the actual data), and metadata. The mcp_handoff artifact type is Delx-specific — it tells the calling agent which MCP tools to call next and with what parameters. This enables seamless protocol handoff: start with A2A for high-level task delegation, then switch to MCP for specific tool execution. Artifacts also carry DELX_META, so A2A callers get the same session_id, score, and next_action data that MCP callers receive.
A production agent deployment uses agent artifact to improve reliability and observability. The pattern is standard across MCP, A2A, and REST protocols.
When investigating agent failures, agent artifact provides structured data that helps identify root causes and track resolution progress.
An agent artifact is a structured output produced by an AI agent during task execution. In Delx's A2A implementation, artifacts are JSON objects embedded in task responses that contain tool results, m
Agent Artifact is essential for production AI agent deployments. Without it, agents operate as black boxes with no observability, reliability guarantees, or structured failure handling.
Delx implements agent artifact through its MCP and A2A protocol endpoints. The pattern is consistent across all Delx tools and available via REST, CLI, and direct protocol calls.