Context Engineering for Coding Agents: Building Better Rules, Skills, and Workflows

If you have ever watched a coding agent do something brilliant and then immediately do something baffling, you have already discovered the real product surface area of agentic development: context. The model is not “smart” in a vacuum. It is smart inside the slice of the world you manage to show it.

Context engineering is quickly becoming a developer skill in its own right: designing what the agent sees, when it sees it, and how it can ask for more without flooding itself into mediocrity.

The Core Insight

The central idea is simple but operationally deep: context engineering is curating what the model sees so that you get a better result. For coding agents, this is not just about a single system prompt. It is an ecosystem of reusable prompts, project rules, tool interfaces, and on-demand “packages” of guidance.

A useful framing is to separate context into two complementary layers:

1) Reusable prompts (instructions vs. guidance).
– Instructions are task-shaped: “Write an end-to-end test for this feature using our conventions.”
– Guidance is policy-shaped: “Prefer small functions; keep tests isolated; do not introduce new dependencies.”

2) Context interfaces (how the agent can get more context).
– Built-in tools (shell, file search, repository reading).
– External integrations (for example via MCP servers).
– “Skills” or similar constructs: bundles of documentation, examples, scripts, and rules that are intentionally lazy-loaded.

What changes the game is the combination: you do not just tell the agent what to do; you give it an internal map of where more knowledge lives, and you decide whether the human, the agent, or the agent software triggers that load.

A second, less obvious insight is that context engineering is a probability amplifier, not a guarantee. The industry language is drifting toward “ensure” and “prevent,” but with LLMs you are still managing likelihoods. The best context setup raises the floor and lowers variance; it does not eliminate failure modes.

Why This Matters

For teams adopting coding agents beyond toy demos, context becomes the difference between:

• An agent that accelerates routine work (tests, refactors, migrations, docs).
• An agent that burns time through rework, subtle regressions, or endless back-and-forth.

There are three concrete impacts.

1) Context is now part of your developer experience (DX) design.
Historically, DX meant linters, CI, templates, and readable code. With agents, DX also includes the shape of your “rules files,” the discoverability of conventions, and the mechanisms that keep prompts current as the codebase evolves. The best teams will treat context like an internal product: versioned, reviewed, and measurable.

2) Bigger context windows do not mean you can dump everything in.
Large context windows tempt people to paste architecture docs, style guides, and a pile of tickets into every session. But oversized context is not free: it increases cost, can reduce relevance, and often makes the agent less coherent. The operational target is not “max tokens.” It is “max signal.”

3) Your codebase itself becomes a training artifact.
Agents read your repository constantly. If the code is inconsistent, undocumented, or full of half-dead patterns, it becomes noisy context. In practice, AI-friendly codebase design is not marketing; it is a productivity multiplier.

Key Takeaways

• Treat context as a system, not a prompt. Combine rules, reusable instructions, tool interfaces, and on-demand skill bundles.
• Separate guidance from instructions. Guidance should be stable and principled; instructions can be more tactical and task-specific.
• Decide who loads context (human vs. agent vs. lifecycle hooks). Automation increases throughput but also increases the need for guardrails and observability.
• Optimize for “smallest sufficient context.” Add context gradually, measure outcomes, and resist copying huge rule sets from strangers.
• Build an escalation path. When the agent is stuck, it should have a predictable way to request more context (specific files, commands, or skills), not hallucinate.

Looking Ahead

Context engineering is heading toward the same maturation arc we saw with CI and infrastructure-as-code:

• Standardization. Teams will converge on familiar files and conventions (a “main rules file,” modular scoped rules, skill bundles).
• Observability. Tooling will make context usage measurable: what was loaded, why it was loaded, and how many tokens each component costs.
• Governance. Prompt and skill changes will require review, because they can silently change behavior across the entire codebase.

A practical recommendation for teams starting now:

1) Create a minimal, high-signal project rules file that captures only the top 10 behaviors you repeatedly correct.
2) Modularize everything else into scoped rules or skills that only load when relevant.
3) Add at least one explicit “risk rule” that the agent must follow (for example: never rotate secrets; never change auth flows without tests).
4) Track failures as “context bugs.” When the agent fails repeatedly in the same way, fix the context, not the agent.

The contrarian point is worth stating: there is a real risk of illusion of control. A complex context configuration can feel like engineering rigor while merely increasing the surface area for contradictions and stale instructions. The best setups are deliberately small, aggressively maintained, and paired with human oversight proportional to the blast radius.

Sources

• Context Engineering for Coding Agents (Martin Fowler) https://martinfowler.com/articles/exploring-gen-ai/context-engineering-coding-agents.html

Based on analysis of Context Engineering for Coding Agents (Martin Fowler) https://martinfowler.com/articles/exploring-gen-ai/context-engineering-coding-agents.html