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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.
Step-by-step guide to install and run Drift's live terminal dashboard, inspect the Go AST analyzer, and test Copilot-driven interactive 'drift fix' suggestions and CI automation.
Install MarginDash (TypeScript/Python/REST) to attribute model usage to customers, link Stripe revenue, and run a pricing-backed cost simulator to find cheaper models and set budget alerts.