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Guide to attach Causari's MCP to an AI coding agent so prompts, model IDs, file reads/writes and reasoning are recorded with bidirectional causal provenance for trace, revert and bisect.
Rafael Lopes argues scaling agents breaks on engineering, not LLM limits: build deterministic guardrails, connectors for unstructured enterprise data, and resilient orchestration.
Practical guide to turning traces, logs, and metrics into compact incident packages so AI debugging agents reason, not filter noise—plus safe read-only and approval-first practices.
Build a repeatable harness that records agents' plan steps, API calls, retries, tokens, wall time and cost to reveal friction points in your library and guide rollout decisions.
Step-by-step guide to run GraphOS locally to capture and inspect LangGraph agent traces, find prompt or tool errors, and debug privately before cloud deployment.
Step-by-step guidance to add two guardrails around each LLM call: pre-LLM redaction/blocking to stop PII leakage and post-LLM verification to catch hallucinations before users see them.
Quickstart to run agent_debugger locally: capture and replay agent sessions, index recurring failures into a searchable memory, and surface smart highlights and drift—see the repo for commands.
Field report: Agent Observatory is a single-process local monitor that ingests OTEL telemetry, sends mobile push alerts, and can stop/restart AI coding agents — built by an AI planner.
Litmus records complete LLM agent executions (prompts, tool calls, outputs) so teams can deterministically replay failures, inject faults, and gate regressions in CI.
Use Ink (ml.ink) to let AI agents push code, generate an MCP/Skill token, and deploy full‑stack apps with auto-detected builds, delegated subdomains, and shared observability.
Run an invoice-and-endpoint audit to recover wasted LLM API spend—community examples show ~60% recoverable using model routing, prompt compression, retry dedupe, and semantic caching.
Record a golden AI-agent tool-call trace with TracePact, diff new runs to spot structural vs argument-only regressions, and gate CI with clear fail/warn reports.