๐ ๐ข๐ฝ๐ฒ๐ป ๐ฆ๐ผ๐๐ฟ๐ฐ๐ฒ ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ผ๐ป๐๐ฟ๐ฎ๐ฐ๐ ๐ ๐ผ๐ฑ๐ฒ๐น ๐๐ฌ.๐ฑ.๐ฌ โ โ๐๐ต๐ฎ๐ ๐ฎ๐ ๐๐ผ๐๐ฟ ๐๐ป๐๐ฒ๐ฟ๐ฝ๐ฟ๐ถ๐๐ฒ ๐๐ผ๐ป๐๐ฟ๐ผ๐น ๐ฆ๐๐ฟ๐ณ๐ฎ๐ฐ๐ฒโ
Agentic Contral Model (ACM) is a spec-first contract layer and open reference runtime for agentic systems. With ACM, a single chat becomes an enterprise control surface: governed contracts, a deterministic runtime, and replay bundles replace ad-hoc tool callsโenabling a true all-in-one app.
๐ฆ ๐ฅ๐ฒ๐น๐ฒ๐ฎ๐๐ฒ: GitHub โ ACM Framework Node v0.5.0. -https://github.com/ddse-foundation/acm/releases/tag/v0.5.0
๐ ๐ฆ๐ฝ๐ฒ๐ฐ ๐ฌ.๐ฑ (๐๐ข๐): https://doi.org/10.5281/zenodo.17278997
๐๐ช๐ต๐ถ๐๐ฒ๐ฝ๐ฎ๐ฝ๐ฒ๐ฟ (๐๐ข๐): https://doi.org/10.5281/zenodo.17279075
๐งญ ๐ข๐๐ฒ๐ฟ๐๐ถ๐ฒ๐:
- Why ACM: https://ddse-foundation.github.io/acm/docs/overview
- How โCapabilities OSโ turns chat into a governed execution surface: https://ddse-foundation.github.io/acm/blog/capabilities-os-chat-with-acm
๐ ๏ธ ๐๐ผ๐ฑ๐ฒ๐ฏ๐ฎ๐๐ฒ & ๐๐ผ๐ฐ๐: Monorepo + guides to "plan โ execute โ replay": https://github.com/ddse-foundation/acm | https://ddse-foundation.github.io/acm/
๐ช๐ต๐ฎ๐โ๐ ๐ถ๐ป ๐๐ฌ.๐ฑ.๐ฌ
โข ๐๐ฑ๐ฆ๐ค-๐ข๐ญ๐ช๐จ๐ฏ๐ฆ๐ฅ ๐ข๐ณ๐ต๐ช๐ง๐ข๐ค๐ต๐ด: Goals, Context Packets, Capability Map, Plans (with alternatives), Tasks/Tools, Policy hooks, Ledger, Replay Bundles.
โข ๐๐ฆ๐ต๐ฆ๐ณ๐ฎ๐ช๐ฏ๐ช๐ด๐ต๐ช๐ค-๐ด๐ต๐บ๐ญ๐ฆ ๐ณ๐ถ๐ฏ๐ต๐ช๐ฎ๐ฆ: Guard evaluation, retries/backoff, verification hooks, checkpointing, append-only decision ledger.
โข ๐๐ฆ๐ฑ๐ญ๐ข๐บ๐ข๐ฃ๐ญ๐ฆ ๐ฅ๐ฆ๐ค๐ช๐ด๐ช๐ฐ๐ฏ ๐ฎ๐ฆ๐ฎ๐ฐ๐ณ๐บ Export a single, audit-ready bundle that captures plans, policy outcomes, checkpoints, and I/O.
โข ๐๐ฏ๐ต๐ฆ๐ณ๐ฐ๐ฑ๐ฆ๐ณ๐ข๐ฃ๐ช๐ญ๐ช๐ต๐บ: Adapters for LangGraph & Microsoft Agent Framework; MCP integration treats external tools as first-class capabilities.
โข ๐๐ถ๐ฏ๐ฏ๐ข๐ฃ๐ญ๐ฆ ๐ฆ๐น๐ข๐ฎ๐ฑ๐ญ๐ฆ๐ด: Entitlement decisions, knowledge acceleration, incident triage, invoice reconciliation, and agent coachingโstreamed planning + replay.
๐ช๐ต๐ ๐ถ๐ ๐บ๐ฎ๐๐๐ฒ๐ฟ๐
Teams get the OpenAI-like chat experience without giving up governance: plans are constrained by a capability map, policies gate every action, and replays certify why an action was allowed and how to reproduce it.
๐๐ฟ๐ฒ๐ฎ๐๐ฒ ๐๐ผ๐๐ฟ ๐ผ๐๐ป
๐๐ผ๐ฟ๐ฒ ๐ก๐ผ๐ฑ๐ฒ ๐ ๐ผ๐ฑ๐๐น๐ฒ๐:
๐ฆ ๐ฐ๐ฒ๐ผ ๐ต๐๐๐๐๐ ๐๐๐ ๐๐ถ.๐ป.๐ถ ๐ฝ๐๐๐ ๐๐ณ๐บ: @ddse/acm-sdk
๐ฆ ๐ฐ๐ฒ๐ผ ๐ต๐๐๐๐๐ ๐๐๐ ๐๐ถ.๐ป.๐ถ ๐ฝ๐๐๐ ๐๐๐๐๐๐๐: @ddse/acm-runtime
๐ฆ ๐ฐ๐ฒ๐ผ ๐ต๐๐๐๐๐ ๐๐๐ ๐๐ถ.๐ป.๐ถ ๐ฝ๐๐๐ ๐ฟ๐๐๐๐๐๐: @ddse/acm-planner
๐ฆ ๐ฐ๐ฒ๐ผ ๐ต๐๐๐๐๐ ๐๐๐ ๐๐ถ.๐ป.๐ถ ๐ฝ๐๐๐ ๐๐๐๐๐๐ข:@ddse/acm-replay
๐ฆ ๐ฐ๐ฒ๐ผ ๐ต๐๐๐๐๐ ๐๐๐ ๐๐ถ.๐ป.๐ถ ๐ฝ๐๐๐ ๐ต๐๐๐๐๐ ๐๐๐: @ddse/acm-framework
๐ฆ ๐ฐ๐ฒ๐ผ ๐ต๐๐๐๐๐ ๐๐๐ ๐๐ถ.๐ป.๐ถ ๐ฝ๐๐๐ ๐ผ๐ฒ๐ฟ: @ddse/acm-mcp
#AgenticAI #AIEngineering #Governance #OpenSource #ACM #LLM #MCP #LangGraph #MAF #Observability #Replayability #EnterpriseAI

