Intent Weaving for AI Coding Agents (www.autohand.ai)

Autohand announces "intent weaving," a practical discipline and runtime for keeping autonomous coding agents aligned with organizational strategy. Instead of treating strategy as prose, intent weaving encodes strategy as typed intents with three layers—vision (why), delivery (what and constraints), and evidence (how success is measured). A public intent schema grounded in Autohand’s knowledge graph lets agents consume mission manifests that include agent rosters, guardrails, telemetry contracts and communication workflows. The system logs diffs, LLM reasoning paths, solver outputs and cryptographically hashed evidence chains (intent hash, execution artifacts, observability snapshots, signatures) so every autonomous action maps back to a business mandate and is auditable by tactical, strategic and audit governance tiers. Technically the approach centers on the Loom model and a mission compiler. The Loom pipeline ingests structured threads (OKRs, runbooks, telemetry), uses LLM-backed parsers to normalize prose into canonical schemas, enriches with graph context, applies constraint solvers to surface conflicts, and emits ranked mission candidates for human review. The mission compiler assigns roles (planner/architect/implementer/verifier), selects guardrails, packetizes work into waypoints, and defines telemetry contracts before Commander spins up swarms. Implications: more realistic enterprise evaluations (scenario replays, guardrail-aware metrics), reduced drift and unsafe automation, and verifiable evidence for compliance—moving benchmarks from toy correctness to durable, policy-respecting autonomy.