Engineering Notes
Thoughts and Ideas on AI by Muthukrishnan
AI Agents
Causal Reasoning and Intervention Planning in AI Agents
How AI agents can move beyond correlation to understand cause and effect, enabling more robust planning, better tool use, and reliable interventions in the real worldAgent 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 accuracyRole Specialization and Crew Based Multi Agent Architectures
How assigning specialized roles to AI agents and orchestrating them as a crew produces better outcomes than monolithic single-agent systemsInverse Reinforcement Learning Inferring Goals From Behavior
Learn how inverse reinforcement learning lets AI agents discover hidden reward functions by observing expert behavior, and why it matters for agent alignment and autonomous systemsWhat 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 contractsError Recovery and Graceful Degradation in AI Agents
Learn how to build resilient AI agents that recover from failures, degrade gracefully, and maintain reliability in production environmentsImitation Learning Teaching Agents by Watching Experts
Learn how AI agents can acquire complex behaviors by observing and mimicking expert demonstrations, from classical behavioral cloning to modern LLM agent distillationKnowledge 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 capabilityBuilding Reusable Agent Capabilities with Skill Libraries
How AI agents acquire, store, and compose reusable skills—from hierarchical reinforcement learning to LLM-based skill synthesis and the emerging paradigm of lifelong learning agents.Improving 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.Diffusion Models and Iterative Refinement in AI Agent Planning
Explore how diffusion models enable AI agents to generate and refine complex action plans through iterative denoising, revolutionizing long-horizon planning and decision-making.Beam Search and Parallel Decoding for Multiple Solution Paths in AI Agents
Master beam search—a powerful technique for exploring multiple solution paths simultaneously in AI agents, from classical NLP to modern LLM reasoning systems.Reward Shaping and Credit Assignment for Guiding Agent Learning
Master the art and science of designing reward functions and solving the credit assignment problem—the key to training agents that learn efficiently and align with human intentions.Tool Composition and Chaining for Complex Agent Capabilities
Master the art of combining simple tools into sophisticated agent capabilities through composition patterns, chaining strategies, and intelligent orchestration.Curiosity-Driven Learning and Intrinsic Motivation in AI Agents
Discover how curiosity-driven learning enables AI agents to explore, learn, and adapt in sparse-reward environments through intrinsic motivation mechanisms.Constraint Satisfaction Problems for Valid Solutions in Complex Agent Planning
Master constraint satisfaction problems (CSP) - a fundamental technique for agent planning, scheduling, and configuration tasks where finding any valid solution is the goal.Consensus Algorithms for Coordinating Agreement in Distributed Agent Systems
When multiple AI agents need to make a collective decision—whether it's agreeing on a shared belief, coordinating a dis...Markov Decision Processes as the Foundation of Sequential Decision-Making
When an AI agent navigates a warehouse, plays a game, or manages a portfolio, it faces a fundamental challenge: making ...How AI Agents Learn from Themselves Through Self-Play and Iterative Refinement
Explore how AI agents improve through self-play and iterative refinement—from AlphaGo's game mastery to modern LLM self-correction techniques.Test-Time Compute and o1-Style Reasoning for Scaling Intelligence Through Inference
We're entering a new era of AI capabilities where **how long** an AI thinks matters as much as **how much** it was trai...Multi-Armed Bandits and the Exploration-Exploitation Dilemma in Agent Learning
Master the fundamental problem of sequential decision-making under uncertainty and learn how AI agents balance trying new actions versus exploiting known rewardsConstrained Decoding and Structured Output for Agent Reliability
Master how modern AI agents ensure reliability through controlled generation—from JSON schemas to grammar constraints and finite-state machines.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.Symbolic Reasoning and Neural-Symbolic Integration in AI Agents
Modern AI agents face a fundamental challenge: how to combine the pattern-matching prowess of neural networks with the ...Contextual Bandits for Balancing Exploration and Exploitation in Real-Time
Master contextual bandits—the algorithm behind personalized recommendations, A/B testing, and adaptive agents. Learn how to balance exploration and exploitation in real-time decision-making.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 judgmentPlanning with Backtracking and Replanning for Resilient Adaptive Agents
Learn how backtracking and replanning enable AI agents to recover from failures and adapt to dynamic environmentsSemantic Routing and Intent Classification in AI Agent Systems
## 1. Concept Introduction ### Simple Explanation Imagine you walk into a massive hospital. Instead of wandering arou...Meta-Learning and Few-Shot Adaptation in AI Agents
How agents learn to learn, adapting rapidly to new tasks with minimal examples through meta-learning and few-shot learning techniques.Agent 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 systemsHow Transformers Power Modern AI Agents with Attention and Reasoning
Understand how attention mechanisms enable AI agents to reason, maintain context, and make intelligent decisions in modern LLM-based systems.Agent Debugging and Observability for Seeing Inside the Black Box
Master the art of understanding, debugging, and monitoring AI agents through tracing, logging, and observability patternsTemporal Difference Learning and Q-Learning as the Engine of Agent Intelligence
Welcome to today's deep dive into one of the most influential algorithms in AI agent programming: **Temporal Difference...Scaling Agent Intelligence Through Specialization with Mixture of Experts
What if instead of one generalist agent struggling with everything, you could dynamically route tasks to specialized ex...Policy Gradient Methods and Actor-Critic Architectures
Master the mathematics and implementation of policy gradient methods and actor-critic architectures—the foundation of modern agent learning systems.Decision-Making Under Partial Observability with POMDPs
Most AI agents operate in messy, real-world environments where they can't see everything. Your autonomous vacuum doesn'...Emergence and Self-Organization When Simple Rules Create Complex Intelligence
Discover how simple local interactions between agents can spontaneously produce sophisticated global behaviors—from ant colonies to distributed AI systems.Building Rational Agents with Human-Like Reasoning Using BDI Architecture
Master the BDI architecture pattern that models rational agent behavior through beliefs, desires, and intentions—a bridge between philosophy and practical AI systems.Bellman Equations and Value Functions for Optimal Decision-Making
## 1. Concept Introduction **In Simple Terms:** Imagine you're playing chess and trying to decide your next move. A be...Vector Embeddings and Semantic Search as the Foundation of Agent Memory
Master the mathematical and practical foundations of vector embeddings—the technology that enables AI agents to remember, search, and reason over vast knowledge bases.Collaborative Problem-Solving in Multi-Agent Systems with the Blackboard Architecture
Explore how the blackboard pattern enables multiple specialized agents to work together on complex problems through shared knowledge spaces and opportunistic reasoning.Goal-Oriented Action Planning for Dynamic Problem Solving in Adaptive Agents
Discover how Goal-Oriented Action Planning (GOAP) enables AI agents to dynamically create flexible plans that adapt to changing conditions, from game NPCs to modern autonomous systems.Auction-Based Task Allocation in Multi-Agent Systems
Learn how auction mechanisms from economics power efficient task distribution in multi-agent systems, from robot swarms to cloud computing.Minimax and Adversarial Search for Decision-Making in Competitive Environments
Master the minimax algorithm and adversarial search - the foundation of game-playing AI agents and competitive decision-making systems.Building 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.Teaching Agents to Learn from Mistakes Through Reflection and Self-Critique
## 1. Concept Introduction **Simple Explanation:** Imagine you're solving a puzzle and you make a wrong move. A good ...Prompt Chaining and Workflow Orchestration for Reliable Multi-Step Agents
When you ask an AI to perform a complex task like "analyze this dataset and create a comprehensive report with visualiz...Behavior Trees for Modular Decision-Making in AI Agents
## 1. Concept Introduction **In Simple Terms:** Imagine a robot butler that needs to serve breakfast. Its decision pro...Hierarchical Task Networks for Planning with Decomposition
## 1. Concept Introduction **In Simple Terms:** Imagine you're planning to "prepare a dinner party." You don't think ...Intelligent Pathfinding for AI Agents with A-Star Search
## 1. Concept Introduction **In Simple Terms:** Imagine you're planning a road trip from New York to Los Angeles. You...Monte Carlo Tree Search from Games to LLMs
## 1. Concept Introduction Imagine you're playing a complex board game like Go. The number of possible moves is astron...Programming Multi-Agent Conversations with AutoGen
## 1. Concept Introduction At its core, **AutoGen** is a framework for simplifying the orchestration, optimization, an...Advanced Problem-Solving with Tree of Thoughts
## 1. Concept Introduction When you solve a simple math problem, you might follow a single, linear sequence of steps. ...An Introduction to Reinforcement Learning for AI Agents
## 1. Concept Introduction At its heart, Reinforcement Learning (RL) is about learning from trial and error. Imagine t...Architecting Short-Term and Long-Term Memory in AI Agents
## 1. Concept Introduction When you read this sentence, you hold its beginning in your mind to understand its end. Tha...Beyond the Training Data with Retrieval-Augmented Generation for AI Agents
## 1. Concept Introduction Imagine taking an exam. In a "closed-book" test, you rely solely on what you've memorized. ...Ensuring Agent Safety with Constitutional AI Guardrails
## 1. Concept Introduction How do we ensure that powerful AI agents behave in ways that are helpful, harmless, and ali...How AI Agents Use Tools with Function Calling
## 1. Concept Introduction Think about a smart assistant like Siri or Google Assistant. When you ask, "What's the weat...Improving Agent Reliability with Plan-and-Solve Prompting
## 1. Concept Introduction Before you cook a complex meal, you read the recipe. Before you assemble furniture, you con...Self-Improvement and Autoformalization in AI Agents That Build Themselves
## 1. Concept Introduction We have explored how agents can reason, plan, remember, act, and collaborate. The final, ul...Solving Complex Problems with AI Agent Swarms
## 1. Concept Introduction Observe a colony of ants. No single ant possesses a grand blueprint. There is no "leader" a...Task Allocation for Machine Teamwork with the Contract Net Protocol
## 1. Concept Introduction How do you get a team to work together without a rigid, top-down manager? Imagine you're a ...Understanding Multi-Agent Behavior with Game Theory and Nash Equilibrium
## 1. Concept Introduction Imagine two partners in crime are captured and held in separate interrogation rooms. The po...Fusing Reasoning and Action in LLM Agents with ReAct
Welcome to our series on mastering AI agent programming. Today, we're exploring one of the most foundational patterns f...Chain-of-Thought Reasoning and Giving LLMs a Thinking Process
Today, we're exploring one of the most impactful yet simple concepts in modern AI: **Chain-of-Thought (CoT) Reasoning**...The Cognitive Loop of AI Agents with Planner Executor and Memory
## 1. Concept Introduction At its core, an AI agent needs to think, act, and remember. The **Planner-Executor-Memory**...