Case Study: Coding Agents — Real-World Case Studies
[ ABORT TO HUD ]
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Case Study: Coding Agents
How Production Coding Agents Work
Coding agents like Claude Code are among the most capable agentic systems. Let's analyze their architecture.
Architecture Overview
| Component | Implementation | Why |
|---|---|---|
| Core Model | Claude Sonnet 5.5 / Opus 5.5 w/ Adaptive Thinking | Best speed/cost/reasoning balance |
| Agent Loop | Custom loop / Claude Agent SDK (query() + ClaudeAgentOptions) | Maximum control over execution |
| Memory | Compaction + project files (CLAUDE.md / AGENTS.md) | Persistent context across sessions |
| Tools | File read/write, bash, search | Full development workflow |
| Safety | Permission system, sandboxed bash | Prevent destructive actions |
Key Design Decisions
- Extended Thinking for Planning: Internal reasoning before multi-file edits reduces errors.
- Tool Parallelism: Multiple file reads happen simultaneously per turn.
- Compaction: Long sessions auto-summarized to prevent context overflow.
- Persistent Memory: Project-specific files store conventions across sessions.
Lessons for Your Agents
- Invest in permissions early - users need trust before granting access.
- Compaction is essential for long-running tasks.
- Project-level context files are simple but powerful persistent "memory."
💡 Key Insight: Top coding agents don't use frameworks. They're custom loops optimized for one use case. Frameworks are training wheels - once you understand the loop, build what you need.
⌨ HANDS-ON LABRun Autonomous Code Editing & Test Loop
⭐ +150 XPSimulate a coding agent (SWE-bench pattern) reading a bug issue, locating the failing test, and verifying the fix.
1Simulate SWE coding agent loop.
OBJECTIVE 1 / 1 — type "hint" if stuck
SYNAPSE VERIFICATION
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What memory strategy do advanced coding agents use?
No memory
Conversation compaction + persistent project files
Fine-tuned model that remembers
10M token context window