Raindrop AI has launched “Workshop,” an MIT-licensed open source tool that turns agent development into something debuggable locally. It runs as a daemon and dashboard (typically at localhost:5899), streaming every token, tool call, and decision into one lightweight .db for fast, private trace review. Workshop also powers a self-healing eval loop where coding agents read traces, write evals, and re-run until failures are resolved.
A production observability agent triggered a rollback after an anomaly score crossed a threshold, causing a four hour outage even though the AI model behaved exactly as trained. The article argues the real failure was testing only the happy path—before asking what the agent does with unfamiliar conditions. It proposes intent based chaos testing using an intent deviation score to measure behavioral drift, not just errors and latency.
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The most costly enterprise AI failures may produce no errors, no red dashboards, and no alerts—yet deliver confident, consistently wrong outputs. The issue isn’t the model’s benchmark performance, but “context decay,” orchestration drift, stale retrieval, and silent partial failures across infrastructure and workflows. Fixing it requires behavioral telemetry and intent-based stress tests, not just uptime monitoring.
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