What You'll Learn
Three independent teams validated the same insight before Google's ADK docs existed: you don't optimize strings — you architect systems.
- Why "prompt engineering" is a misnomer that limits your thinking
- The context architecture pattern that makes agents actually work
- How dependency chains create predictable agent behavior
- Real metrics from shipping this in production
Why This Matters Now
Everyone's building agents. Most are failing. The failure mode is always the same: the agent "forgot" something, the agent went off-script, the agent hallucinated a tool that doesn't exist.
These aren't AI problems. They're architecture problems.
The agent only knows what you put in its context window. If your context is a mess, your agent is a mess.
Prompt engineering treats the context window like a text box. Context engineering treats it like a system with inputs, state, and dependencies.
The Mechanism
Context engineering has three layers:
1. Static Context (The Rails)
Instructions that never change during execution. System prompts, role definitions, tool schemas. These are your CLAUDE.md files, your skill definitions, your agent personas.
2. Dynamic Context (The State)
Information that changes as the agent works. Current task, completed steps, retrieved documents, user preferences. This is what gets injected mid-conversation.
3. Retrieved Context (The Memory)
Information pulled from external systems. Database queries, API responses, vector search results. The agent doesn't "know" this — it retrieves it when needed.
The key insight: these layers have dependencies. Dynamic context depends on static context. Retrieved context depends on both. Get the order wrong, and the agent hallucinates.
Evidence
We shipped this pattern in Squad's orchestration layer. Results:
- 87% autonomous approval rate (up from 23% with prompt-only approach)
- 4.5 hour sustained operations without human intervention
- Zero hallucinated tools in production (strict schema validation)
The difference wasn't better prompts. It was better architecture.
Tradeoffs
What you give up:
- Speed of iteration — architecting context takes longer than tweaking prompts
- Flexibility — rigid context structures resist ad-hoc changes
- Simplicity — more moving parts to understand and debug
What you gain:
- Predictability — agents behave consistently across runs
- Debuggability — when something breaks, you can trace why
- Scalability — patterns that work for one agent work for N agents
How to Apply This
Step 1: Audit your current context. What's static? What's dynamic? What's retrieved?
Step 2: Map dependencies. Does your dynamic context reference things from static context? Does retrieved context assume dynamic state?
Step 3: Enforce load order. Static first, then dynamic, then retrieved. Never violate this.
Step 4: Add validation. Before the agent acts, verify all required context is present. Fail fast if something's missing.
What's Next
This is just the foundation. The next evolution is context contracts — formal specifications for what context each agent needs, validated at runtime.
We're building this into Squad. More soon.
