Autonomous Agent LoopsCheatsheet
Build robust ReAct autonomous agent loops with termination conditions, state history management, and recursion limits.
Direct, one-page reference guides to build software and AI systems, grow personal wealth, and optimize everyday health — designed for quick lookups and visual learners.
Build robust ReAct autonomous agent loops with termination conditions, state history management, and recursion limits.
Connect Node.js and TypeScript applications to modern AI APIs using official SDK clients and secure environment authentication.
Transcribe spoken audio, generate timestamped subtitles, and synthesize natural text-to-speech with Whisper and OpenAI.
Elicit step-by-step reasoning, mathematical problem solving, and complex logic through explicit chain-of-thought prompting.
Implement recursive character chunking, semantic paragraph splitting, Cohere Rerank cross-encoders, and rank fusion.
Generate verified software code, validate syntax via AST parsing, and execute sandboxed scripts with E2B and Docker.
Manage token budgets, rolling conversation history, semantic compaction, and prompt summarization in production AI systems.
Build resilient AI integrations with exponential backoff, rate limit recovery, circuit breakers, and fallback models.
Build automated test suites, implement LLM-as-a-judge rubrics, and track accuracy benchmarks for AI pipelines.
Guide model reasoning, tone, and formatting consistency using balanced input-output in-context demonstration exemplars.
Prepare formatted training datasets, execute fine-tuning jobs, and evaluate specialized models with OpenAI and LoRA.
Implement content moderation, PII redaction, output schema guardrails, and refusal cascades for safe AI deployment.
Implement human authorization approval gates, state persistence, and audit controls for high-stakes AI tool actions.
Standardize AI agent tool integrations and resource access using Anthropic open-standard Model Context Protocol.
Select the optimal artificial intelligence model tier by evaluating latency, context size, reasoning capacity, and token costs.
Monitor LLM latency, track token expenses, trace multi-agent execution spans, and instrument OpenTelemetry telemetry.
Execute multiple concurrent tool requests simultaneously using Promise.allSettled and rate-limited batch workers.
Slash AI latency and token costs by up to 90 percent using Anthropic and OpenAI prompt caching breakpoints.