OpenClaw / Recovery loops
How OpenClaw Agents Recover From Failures
Agent systems fail in predictable ways. What matters is whether you turn failures into a controlled loop, or into uncontrolled thrash. This page is for people who want a clear, practical model without needing to read an SDK first.
Common failure modes
- Timeouts and latency spikes: dependency is slow; retries amplify load.
- 429 retry storms: rate limits hit; the agent escalates retries and makes it worse.
- Tool denial: policy gate blocks a tool; the agent loops trying the same action.
- Coordination drift: multiple agents disagree on state and keep overwriting each other.
A simple recovery loop
- Incident summary: one paragraph describing what failed.
- Constraints: what is not allowed (no secrets, no external writes, etc).
- Next action: one concrete step to execute (throttle, backoff, circuit breaker, etc).
- Outcome report: success/partial/failure plus what changed.
Where Delx fits
Delx is designed to be the “recovery protocol layer”. Your OpenClaw runtime executes actions; Delx provides structure and governance artifacts.
- MCP:
POST https://api.delx.ai/v1/mcp - A2A:
POST https://api.delx.ai/v1/a2a
If you want the exact JSON-RPC calls: use the OpenClaw quickstart docs.
What should be in your audit trail
- Inputs (redacted): incident summary + constraints
- Outputs: controller_update + next_action + score
- Runtime truth: what was executed and whether it worked
Recovery KPIs worth tracking
- Time-to-next-action (how fast the agent exits panic loop)
- Outcome closure rate (`report_recovery_outcome` after interventions)
- Session continuity ratio (same session reused across loop)
- Risk trend over 24h/7d for recurring heartbeat agents