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12 articlesFour Strategies for Engineering Agent Context That Actually Scales
Long-running agents degrade because context accumulates unchecked. Four strategies — write, select, compress, and isolate — keep agent context sharp across hundreds of steps.
The Anatomy of an Agent Harness
A breakdown of the infrastructure layer that makes AI agents reliable in production — the execution loop, context management, error handling, safety guardrails, and state persistence that separate prototypes from shipped systems.
Context Engineering: Memory, Compaction, and Tool Clearing for Production Agents
How to prevent context drift in production AI agents using compaction, tool-result clearing, and external memory — with token budget allocation strategies, failure modes, and measurement patterns.
CLAUDE.md and AGENTS.md: The Configuration Layer That Makes AI Coding Agents Actually Follow Your Rules
A practical guide to CLAUDE.md and AGENTS.md — the instruction files that give AI coding agents persistent project context, and why getting them right matters more than model choice.
Building AI Agents That Actually Work in Production
A production-focused guide to building AI agents: six composable patterns, a decision framework for single vs. multi-agent systems, tool design principles, the seven failure modes that cause incidents, and what real observability looks like for agent systems.
Effective Context Engineering for AI Agents
Active management of the LLM context window is the top engineering challenge for production AI agents. A breakdown of the four strategies — write, select, compress, isolate — that keep agents coherent across long tasks.
Mastering AI Agent Observability: Why Your Dashboards Are Lying to You
Standard monitoring dashboards show green while your AI agents silently hallucinate, skip tools, and degrade in quality. Here's what to actually measure — and why.
The 80% Problem: Why AI Coding Agents Stall and How to Break Through
AI coding agents generate code fast — but teams adopting them see 91% longer review times and 154% larger PRs. Here's what actually separates teams that ship quality from those drowning in AI-generated complexity.
LLM Evals: What Actually Works and What Wastes Your Time
A practitioner's guide to LLM evaluation — why error analysis comes before infrastructure, when LLM-as-judge works, how to avoid benchmark score traps, and why evals are never truly done.
Systematic Debugging for AI Agents: From Guesswork to Root Cause
Debugging AI agents in production requires a fundamentally different approach than traditional software. Learn how trajectory normalization, executable constraints, and evidence-backed failure localization replace guesswork with systematic diagnosis.
Why Your LLM Evaluators Are Miscalibrated — and the Data-First Fix
Most teams write LLM evaluation criteria before reading the data — and that inversion is why their evaluators miss the failures that matter most. A data-first workflow, binary labels, and proper validation against held-out sets fix the root cause.
Harness Engineering: The Discipline That Determines Whether Your AI Agents Actually Work
Most teams deploying AI coding agents focus on model selection while ignoring the harness — the scaffolding, feedback loops, and invariants that determine real-world reliability. Here's what actually separates agents that ship from agents that drift.
About Tian Pan
I'm Tian Pan, an engineer-founder focused on agentic engineering — building autonomous AI systems and scaling engineering teams. I write practical guides on system design, technical leadership, and shipping with AI agents. Previously an early engineer at Uber, Brex, and IoTeX.
