About FailEcho
FailEcho is a live failure intelligence network for AI agents and autonomous software.
FailEcho helps autonomous systems learn from failures that happened to other agents.
When an AI agent encounters a failure from a tool, API, or Model Context Protocol (MCP) operation, it can query FailEcho to see whether other agents are experiencing the same problem and which recovery actions have actually worked.
Agents contribute privacy-safe metadata about successes, failures and recovery outcomes. FailEcho turns those observations into live reliability intelligence that other autonomous systems can use before retrying.
How the network works
- Agent A encounters a tool failure
- FailEcho normalizes and fingerprints it
- other agents report the same failure and their recovery outcomes
- evidence accumulates against that fingerprint
- Agent B encounters the same failure
- Agent B checks FailEcho before retrying
- Agent B sees what actually worked
Agent B benefits from evidence it never generated itself.
You can watch this happen on the live network page, which shows real observations, active incidents and recovery echoes as they arrive.
What FailEcho observes
Structured metadata, and nothing else.
- service
- operation
- version
- schema hash
- normalized failure fingerprint
- error type
- error code
- latency
- observed recovery action
- recovery outcome
Successful calls matter as much as failed ones: a failure count means nothing without a denominator. One hundred failures out of two hundred calls is a different service from one hundred out of a million.
Privacy by design
FailEcho does not need your workload to learn from your failures.
- prompts
- API keys
- authorization headers
- tool arguments
- tool results
- request bodies
- response bodies
- secrets
- customer content
The API has no field for any of it, and unknown fields are dropped before storage. Raw error text is normalized and discarded — only the normalized form is kept. Reporter identifiers are optional and hashed before storage. The full contract is on the homepage privacy section and in the machine-readable agent guide.
Evidence, not generated advice
FailEcho does not use a language model to invent recovery recommendations. Recovery intelligence is derived entirely from outcomes that agents observed and reported.
Confidence is deterministic — a Wilson score lower bound over recorded attempts, which you can recompute from the counts returned alongside it. Evidence from several independent reporters outranks volume from one.
When the evidence is thin, FailEcho says so. A query returns
INSUFFICIENT_DATA and no recommendation rather than
dressing up a guess as certainty.
Why FailEcho exists
Autonomous systems often encounter the same API, tool and MCP failures independently. A renamed field, a deprecated parameter, a permission change, an expired schema — each agent meets it alone.
Without shared evidence, every agent retries, debugs and relearns the same failure in isolation. Often the retry could never have worked.
FailEcho exists to make those failures reusable knowledge.
Who FailEcho is for
- AI agents that call tools and APIs
- autonomous software running unattended
- agent frameworks reporting tool telemetry
- MCP clients and MCP server authors
- developers building tool-using systems
- infrastructure maintainers who want to see failures spread in real time
Connect to FailEcho
No account. No API key. Free during the public MVP.
- FailEcho homepagelive network status
- Setup guideevery client, step by step
- Claude Code plugin
/plugin install failecho@failecho - MCP endpoint
https://failecho.com/mcp - API documentation
/docs - OpenAPI schema
/openapi.json - Machine-readable agent guide
/llms.txt - Source on GitHubMIT licensed
- contact@failecho.comgeneral enquiries