Enterprise-Grade AI Agent Harness Engineering: From Demo to Production
This scenario targets engineering teams, addressing engineering challenges when transitioning AI programming from demo to enterprise-level production systems, such as AI amnesia, context pollution, and uncontrollable code quality. The core approach is to build a five-layer memory system, Hooks quality gates, and dynamic workflows based on Claude Code, leveraging structured context, deterministic validation, and orchestration patterns to enable AI to stably, controllably, and verifiably complete long-cycle tasks in million-line codebases. Benchmarks show that the same model, optimized via Harness, can jump from below baseline to Top 5.
Steps
- 1
Establish a five-layer memory system: Create an Enterprise-level CLAUDE.md for security and compliance policies; Project-level files limited to 200-300 lines for team norms; Rules-level for path-conditional loading of detailed specifications; Local-level for personal notes, added to .gitignore.
- 2
Configure context triage mechanism: Classify candidate information into four levels P0-P3, injecting only core logs and historical ticket handles into context, reducing token consumption from 18K to 2K, improving signal-to-noise ratio.
- 3
Implement structured input and Stop Hook gate: Avoid vague prompts; provide specific functions and line numbers; configure Stop Hook to automatically run lint and unit tests; block submission if tests fail and let AI self-heal.
- 4
Package declarative Skill assets: Save commonly used prompts as Skill files in the .claude/skills/ directory, enabling team sharing via Git version control; new members can clone the repo to inherit all AI programming capabilities.
- 5
Enable dynamic workflows for complex tasks: Use the ultracode command to trigger; Claude generates a JavaScript orchestration file based on the task context, supporting six modes including classification execution, fan-out synthesis, and adversarial validation; tasks can resume from breakpoints.
- 6
Build Loop Engineering automation system: Design Automations to trigger tasks on schedule, combined with Worktrees for parallel isolation, Skills for knowledge solidification, and Sub-agents for division of labor; persist memory to disk rather than context.
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