Engineering Notes
Thoughts and Ideas on AI by Muthukrishnan
AI Agents
Agent Loops Explained: The Simple Pattern Behind Useful AI Agents
A practical explanation of agent loops, how they differ from single-pass LLM calls, and why the simple think-act-observe cycle powers useful AI agents.Engineering Digital Twin - The Next Billion-Dollar AI Category
Why the biggest AI opportunity in software engineering is not code generation, but a living model of the engineering organization that helps leaders make better decisionsThe Unsolved Engineering Problems of AI Agents
A practical roadmap of the core engineering problems that must be solved before AI agents become reliable, scalable, secure, and economically useful systems.Working Memory Compression and Context Distillation in Long Horizon Agents
How long-running agents compress, distill, and selectively retain working memory to operate effectively within finite context windowsSpec-Driven Development with spec-kit: Stop Vibe Coding, Start Specifying
A complete tutorial on using GitHub's spec-kit to bring structure to AI-assisted development — from install to your first specification.Agent Evaluation and Benchmarking for Measuring What Matters
Learn how to systematically evaluate AI agent performance using benchmarks, metrics, and evaluation frameworks that go beyond simple accuracyWhat If GitHub Was Built for AI Agents
Reimagining source code management from the ground up for AI agents, with intent based commits, simulation before merge, agent reputation, and automatic rollback contractsKnowledge Distillation Teaching Smaller Agents From Larger Ones
Learn how knowledge distillation enables large, expensive AI agents to teach smaller, faster ones — reducing cost and latency while preserving capabilityImproving Decisions Through Multi-Agent Debate and Deliberation
How AI agents can reach better decisions by arguing with each other—exploring debate protocols, deliberation architectures, and the surprising power of constructive disagreement.Verification and Validation Loops for Agent Reliability Through Runtime Checks
Learn how modern AI agents use verification and validation loops to ensure output quality, catch errors at runtime, and build reliable production systems.Human-in-the-Loop Agents for Bridging Autonomy and Oversight
Master the patterns and practices of human-in-the-loop agent systems that balance automation with human judgmentAgent Communication Protocols and Message-Passing Patterns for Coordination
Master the protocols and patterns that enable AI agents to communicate, coordinate, and collaborate effectively in multi-agent systemsAgent Debugging and Observability for Seeing Inside the Black Box
Master the art of understanding, debugging, and monitoring AI agents through tracing, logging, and observability patternsBuilding Stateful AI Systems with LangGraph and Agentic Workflow Graphs
Learn how to build complex, stateful AI agent systems using graph-based architectures with LangGraph—a paradigm shift from linear chains to cyclic, controllable workflows.Monte Carlo Tree Search from Games to LLMs
## Concept Introduction **Monte Carlo Tree Search (MCTS)** is a heuristic search algorithm for finding optimal decisio...Architecting Short-Term and Long-Term Memory in AI Agents
## Concept Introduction AI agents require structured memory to operate effectively across turns and sessions. Without ...Ensuring Agent Safety with Constitutional AI Guardrails
## Concept Introduction **Constitutional AI (CAI)** is a method for training an AI to supervise itself. Rather than re...How AI Agents Use Tools with Function Calling
## Concept Introduction **Tool Use** is the mechanism that allows a language model to interact with and affect the out...Chain-of-Thought Reasoning and Giving LLMs a Thinking Process
### Concept Introduction **Chain-of-Thought (CoT) Reasoning** is a prompting strategy that encourages a model to decom...