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Raindrop AI

Agent failure detection

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What Raindrop is

Raindrop is monitoring built specifically for agents. It traces each run through every message and tool call, groups recurring failures into issues, and adds simulation so a proposed change can be compared against real traffic before it is merged.

What you can do with it

  • Trace messages and tool calls in a run
  • Group recurring failures into issues
  • Investigate root causes with original traces
  • Simulate changes before merging
  • Verify improvements against real behaviour

Who it is for

  • Teams shipping agents to many users
  • Engineering teams running agents in production
  • Organisations needing compliance documentation

What to watch out for

  • Traces contain the full content of user interactions, so redaction, access control and retention are critical
  • Compliance claims such as a named certification should be verified against the actual report, not the marketing page
  • Simulation covers the scenarios you script, not everything real users do
  • Vendor figures about trace volume describe their platform, not your outcomes

Pros & cons

✓ What we like

  • True end-to-end traces of agent runs
  • Regressions caught before merge
  • Issue grouping reduces noise

! What to watch out for

  • Trace data is highly sensitive
  • Certification claims need verification
  • Simulation is scoped by your scripts

FAQ

What does it trace?

Every message and tool call in an agent run, with the original traces kept as evidence.

Can it prevent failures?

It detects patterns and simulates changes, which reduces rather than eliminates failures.

What governance is needed?

Redaction, access control and retention for full interaction traces.

Last reviewed: 2026-09-19

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