Intent Weaving for AI Coding Agents

9 hours ago

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Intent weaving translates organizational strategy into executable actions for AI coding agents, ensuring alignment with business goals.
The process involves capturing strategy, encoding governance, and guiding autonomous agents to act within defined bounds.
Key components include the Loom Model for integrating strategic, operational, and signal inputs into a cohesive mission plan.
The mission compiler assigns roles, selects guardrails, and designs human interfaces to ensure accountability and oversight.
Evidence chains provide verifiable proof that autonomous actions align with encoded intent, supporting compliance and audit requirements.
Current AI coding benchmarks are criticized for lacking real-world complexity, such as governance constraints and multi-step workflows.
Common failure patterns of LLM coding agents include code reliability issues, poor context awareness, and weak collaboration practices.
Anti-patterns like intent by committee and invisible overrides have been addressed through structured templates and logging mechanisms.
Tooling supports intent authors with contextual recommendations, simulation previews, and live risk scoring to improve intent quality.
Future developments may include predictive simulation, cross-organizational intent sharing, and automated semantic reconciliation of strategic statements.

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