Context engineering is the practice of designing what AI agents see, how their outputs connect, and where quality boundaries sit in multi-agent coordination. It's not prompt engineering. Prompts are instructions; context is the information architecture agents operate within.
The problem context engineering solves
When you use AI agents without structured context, each agent produces locally reasonable output. A code generation agent writes working functions. A review agent finds real issues. A planning agent produces sensible plans. But the outputs don't cohere. The code doesn't match the plan. The review catches issues the plan already addressed. Everyone is "right" and the result is a mess.
Context engineering fixes this by designing the shared information space that all agents operate within. When the code agent and the review agent see the same architectural constraints, scope boundaries, and quality criteria, their outputs align without manual reconciliation.
What it looks like concretely
Here are the patterns that made the biggest difference in practice:
- Scope fencing. Every Build Loop starts with explicit scope and non-goals. Agents that receive both produce output that stays within boundaries. Agents that only receive scope tend to expand into adjacent concerns.
- Decision history. When agents can see prior decisions and their rationale, they avoid re-litigating settled questions. Without decision history, each agent interaction starts from zero and may reach different conclusions.
- Role boundaries. Defining what each agent is and isn't responsible for prevents both gaps and overlaps. A staff engineering agent shouldn't also be doing product prioritization; a PM agent shouldn't be making architecture choices. Clear boundaries produce cleaner handoffs.
- Output contracts. Specifying the format, depth, and scope of what each agent should return makes outputs composable. When the PM agent's output feeds into the engineering agent's context, a structured contract prevents information loss or format mismatches.
Failure modes I've hit
The most common failure: over-contexting. Dumping the entire project history into every agent's context doesn't help — it dilutes the relevant signal and makes agents less focused, not more informed. The right context is minimal and relevant, not maximal and complete.
Another pattern: context that's stale. When agents operate on yesterday's architecture decisions while today's code has moved past them, the outputs conflict. Keeping context current is an active maintenance task, not a set-and-forget configuration.
The subtlest failure: assuming agents will infer boundaries you haven't stated. They won't. If the context doesn't include non-goals, agents will expand scope. If it doesn't include quality criteria, agents will optimize for completeness rather than correctness. Every unstated assumption is a source of drift.
The iteration cycle
Context engineering isn't a one-time design exercise. Every Build Loop reveals new coordination patterns, edge cases, and opportunities. The process: run the loop, observe where outputs diverged or failed to cohere, diagnose the context gap, and adjust for the next cycle.
Over time, the adjustments compound. Scope fencing that started as manual copy-paste is now templated. Role boundaries that were implicit are now explicit in agent specifications. Output contracts that were ad hoc are now standardized skills. The system gets more reliable because the context architecture gets more precise.