Agents

What Does It Mean to Debug?
What Is an Agent, Really?
The First Divergence
Evidence Before Explanation
The Debugging Stack
Reading Python Exceptions
Inspect State, Don't Guess
Debug the Boundary
Assertions, Invariants, and Contracts
Environment Bugs
The Notebook Is Not the Program You See
Hidden Notebook State
Reproducible Notebooks
Debug the Data Before the Model
Shapes, Types, Devices, and Tensors
The Agent Writes Its Own Context
When Training Goes Wrong
Agents Are Programs Too
Debugging Evaluation
Tools Produce Context
Debugging What You Cannot See
Search the Reasoning Space
Is the Model Actually the Problem?
Inspect the Actual Model Input
Context Windows and Truncation
Build a Self-Improving Engineering Program
Sampling Is Part of the Program
Internal Signals
Representation and Behavioral Diffs
AI as Builder, Designer, Researcher, and Reviewer
Debugging Intent
Debugging Context for Coding Agents
Debugging AI-Generated Designs
Debugging AI Research
Debugging Coding Agents
Treat Prompts as Programs
Minimize the Prompt
Retrieval Is a Pipeline
Retriever Failure or Generator Failure?
Debugging Hallucinations
The Model's Explanation Is Not a Trace
An Agent Is a Trajectory
Trace the Agent
Agent Failure Taxonomy
Loops, Thrashing, and Retry Storms
Time Travel, Replay, and Forking
Causal Replay
Trajectory Diff
Multi-Agent Systems
Can One AI Debug Another?
The AI Crash Dump
Diagnostic AI Invariants
From Symptom to Hypotheses
Discriminating Experiments
How Do You Know the Diagnosis Is Right?
AIDebugBench
Debug the Debugger
AI Observability
From Production Failure to Regression
Runtime Invariants and Guardrails
Debugging Cost and Latency
Debugging in Production
The Ten-Minute Debug
The One-Hour Investigation
The Full AI Incident Investigation
The Debugging AI Toolkit
Build a Production AI Agent From First Principles: The Complete Reference Architecture
Advanced Agents From First Principles 24: How Do You Release Agent Behavior Safely? Add Behavioral Contracts, Compatibility Checks and Promotion Gates
Advanced Agents From First Principles 23: Your Infrastructure Is Healthy. Why Is the Agent Getting Worse? Detect Behavioral Drift and Roll Back Safely
Advanced Agents From First Principles 22: What Happens When One Dependency Starts Failing? Add Circuit Breakers, Bulkheads and Graceful Degradation
Advanced Agents From First Principles 21: What Happens When Too Many Agents Compete for the Same Resources? Add Admission Control, Quotas and Backpressure
Advanced Agents From First Principles 20: Can Your Agent Coordinate Across Machines Without Duplicating Work? Use Leases, Idempotency and Fencing
Advanced Agents From First Principles 18: What Should Your Agent Observe Next? Use Expected Value of Information
Advanced Agents From First Principles 17: What Is Your Agent Actually Uncertain About?
Advanced Agents From First Principles 16: Where Should an Agent Spend Its Compute? Build a Dynamic Budget Scheduler
Advanced Agents From First Principles 15: How Do You Optimize an Agent Policy Without Turning It Into Another Black Box?
Advanced Agents From First Principles 14: Can Your Agent Learn From Its Own Trajectories Without Learning the Wrong Lessons?
Advanced Agents From First Principles 13: How Do You Debug an Agent That Made the Wrong Decision? Add Trajectory Observability
Advanced Agents From First Principles 12: Is Your Advanced Agent Actually Better? Benchmark It Under Equal Budgets
Advanced Agents From First Principles 11: Which Advanced Agent Architecture Should You Use? A Practical Selection Guide
Advanced Agents From First Principles 08: Is Your Agent Spending the Same Compute on Every Task? Build Adaptive Agents That Escalate Only When Needed
Advanced Agents From First Principles 25: Can You Reproduce an Agent Run Months Later? Add Deterministic Replay and Provenance
Advanced Agents From First Principles 10: Are You Combining Every Agent Technique Into One Monster? Build a Mixture-of-Agents Runtime
Advanced Agents From First Principles 09: Can Your Agent Actually Learn From Previous Runs?