Breakthrough ideas start where others left off.

$ claude

> I want to build a system that auto-discovers runtime bugs in my production
> agents used by Keelo AI customers. Use TruthAPI to find related use cases,
> methods, technologies, and builders who have done something similar.

Claude
I'll search production systems, evaluation methods, observability patterns,
automatic bug discovery, and remediation approaches.

tool: truthapi.getSummaries({
  query: "runtime bugs production agents observability evaluation remediation",
  sources: ["docs", "github", "youtube_video", "academic_paper", "reddit_post"],
  total: 50
})

TruthAPI
243 relevant items found.

The strongest production pattern is a closed loop:

instrument with OpenTelemetry / OTLP
→ capture complete execution traces
→ run layered evals over the trace corpus
→ cluster and anomaly-detect failures
→ verify adversarially
→ open a fix PR

Relevant systems and methods:
- Langfuse, LangSmith, Arize Phoenix, OpenLIT, Braintrust
- Insights Generator for corpus-level trace diagnostics
- Hodoscope for unsupervised behavioral monitoring
- AgentEval for execution-DAG evaluation
- Aient AI for OTel ingest → grouped errors → fix PRs
- nightly production error sweeps

Production warning:
AnyPoC shows why an LLM must not be the only judge of whether it found a bug.
The model can generate a convincing but invalid proof.

Claude
The architecture should not be "ask an LLM if this is a bug."

trace every execution
→ cluster failures across the corpus
→ use anomalies only for triage
→ synthesize a deterministic reproduction
→ confirm with an executable test
→ only then classify and fix

> Tell me more about Sandipan Bhaumik and what he has done that is relevant.

tool: truthapi.related360({
  query: "Sandipan Bhaumik",
  hops: 2,
  total: 50
})

TruthAPI
Most relevant production ideas:
- evaluation before model selection
- deterministic, semantic, and behavioral evaluation
- observability as first-class infrastructure
- living golden datasets
- production incident playbooks
- cost governance

Provenance caveat:
Everything attributed to him traces to one conference talk. Treat it as one
experienced practitioner viewpoint, not independently corroborated research.

Claude
Most applicable idea for Keelo:
Evaluate the execution trajectory, not only the final output. Many production
bugs appear only in duplicate tool calls, retry loops, and inefficient paths.

> Has anyone used reinforcement learning for bug solving?

tool: truthapi.getSummaries({
  query: "reinforcement learning automated bug solving SWE-bench RLVR",
  total: 50
})

TruthAPI
Active approaches include Kimi-Dev-72B, BoostAPR, CODESKILL,
Globant Code Fixer, and RLVR-style systems.

Claude
Recommendation: do not begin by training an RL bug fixer.
Use off-the-shelf coding agents. Invest first in the verifier:
- deterministic reproduction
- golden regression cases
- semantic and behavioral evals
- bounded remediation loops

Result: a phased architecture for runtime traces, failure taxonomy,
nightly corpus diagnostics, deterministic verification, fix proposals,
rollback, and recurrence monitoring.