- 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?
- Agents From First Principles 00: What Is an Agent, Really?