Context Engineering for Coding Agents: The Complete 2026 Guide

Your AI coding agent is only as good as the context you give it. And in 2026, getting that context right has become an art form.

Martin Fowler’s team just published a comprehensive breakdown of how context engineering works in modern coding agents. If you’re not thinking strategically about this, you’re leaving serious productivity gains on the table.

The Core Insight

Context engineering isn’t just “put stuff in the prompt.” It’s a sophisticated discipline with its own vocabulary, patterns, and best practices. The simple definition: “Context engineering is curating what the model sees so that you get a better result.”

But the devil is in the details. Modern coding agents like Claude Code now offer an explosion of configuration options—CLAUDE.md, rules, skills, subagents, MCP servers, hooks, and plugins. Each serves a different purpose, loads at different times, and has different trade-offs.

The key dimensions to understand:

1. Instructions vs. Guidance – Instructions tell the agent what to do (“Write an E2E test this way”). Guidance tells it how to behave (“Always write independent tests”).

2. Who decides to load context – The LLM, the human, or the agent software itself? This affects automation vs. control.

3. How much context – Bigger isn’t better. Too much context degrades performance and increases costs.

Why This Matters

The coding assistant landscape is converging on a shared paradigm. What Claude Code pioneered, others are rapidly adopting. Understanding these patterns now means you’ll be productive with any modern coding agent.

Here’s the practical taxonomy:

Always-on context (CLAUDE.md)
– Loads at session start
– Best for: universal conventions (“use yarn, not npm”)
– Keep it small—this burns tokens every session

Path-scoped rules
– Load when matching files are touched
– Best for: language-specific conventions (“bash variables as ${var}”)
– Modular, efficient, scales with codebase size

Skills (the game-changer)
– LLM decides when to load based on task relevance
– Can include guidance, instructions, documentation, even scripts
– Use for: API integrations, component conventions, workflow documentation
– This is replacing slash commands entirely

Subagents
– Run in separate context windows, can use different models
– Best for: E2E testing, code review (second opinion without session baggage), parallel workloads
– Foundation for advanced orchestration patterns

MCP Servers
– Give agents access to external APIs and tools
– Being superseded by skills that describe CLI usage
– Still useful for: browser automation, complex integrations

Key Takeaways

• Build context incrementally. Don’t dump everything upfront. Models have gotten powerful enough that half-year-old workarounds may be unnecessary.

• Be strategic about what loads when. Always-on context is expensive. Use skills for lazy-loading.

• Sharing context is tricky. What works for your team may not work for strangers. Build iteratively, don’t copy-paste from the internet.

• This isn’t really “engineering.” Despite the name, there’s no unit testing for context. You can increase probability of good results, but never guarantee them.

• Beware the illusion of control. Context engineering improves outcomes—it doesn’t “ensure” or “prevent” anything. LLMs are probabilistic. Human oversight still matters.

Looking Ahead

We’re in a “storming” phase. The current explosion of features will likely consolidate—skills may absorb rules and slash commands, simplifying the mental model.

But the core insight will remain: your agent’s effectiveness depends on what it knows. Master context engineering now, and you’ll be ready for whatever consolidation brings.

The teams that treat context configuration as infrastructure—versioning it, reviewing it, iterating on it—will dramatically outperform those who ignore it. This is the new meta-skill for AI-augmented development.

Based on analysis of “Context Engineering for Coding Agents” from martinfowler.com