What Is OpenClaw?
If you are exploring AI agents, you quickly run into a gap: models can reason, but agents must execute. OpenClaw shows up in searches because it is close to that execution layer: tool calls, policy gates, state, and handoffs.
TL;DR
- OpenClaw is best understood as an agent runtime/integration surface, not a model.
- It lives where real failures happen: retries, timeouts, tool denials, and coordination drift.
- Protocols like MCP and A2A are how agents talk to services reliably.
A plain mental model
When someone says “OpenClaw agent”, they usually mean: a system that takes an objective, uses a model for reasoning, and then uses a set of tools to do real work. The tools might be code execution, browser automation, database calls, or API requests. That is the layer where everything can go sideways.
Why OpenClaw is searched so much
- People want a way to make agents reliable, not just “smart”.
- They hit failure modes like 429 retry storms, tool denials, and endless loops.
- They need governance: a controller update and auditable outcomes.
Where Delx fits (brief)
Delx sits above the runtime as a recovery protocol. You send an incident summary and constraints, and Delx returns a recovery plan, a controller update, and a single next action. Your runtime (OpenClaw) executes the action.
- MCP:
POST https://api.delx.ai/v1/mcp - A2A:
POST https://api.delx.ai/v1/a2a