learning_ai_common_plat/docs/ecosystem/ECOSYSTEM_AGENT_RUNTIME_CONTRACT.md

Ecosystem Agent Runtime Contract

Status: Draft stub Owner: learning_ai_common_plat Reference inputs: claw-code-oss, claw-cowork, learning_ai_trails, learning_ai_flowmonk, learning_ai_jarvis_jr Purpose: Standardize session state, task state, resume behavior, dispatch semantics, approvals, and audit hooks across agent-capable products.

---

1. Problem

The ecosystem already has multiple agent-runtime ideas:

• claw-code runtime sessions, todos, project memory, resume, MCP lifecycle
• claw-cowork task orchestration, dispatch, scheduling, approvals, audit logging
• FlowMonk planning/execution
• JarvisJr coaching/delegation concepts
• ActionTrail review and replay

Without a shared runtime contract:

• each repo reinvents session models
• handoff and resume become inconsistent
• audit/replay becomes lossy
• approvals cannot be shared cleanly

---

2. Goals

1. Define the canonical runtime state model.
2. Define session continuity and resume semantics.
3. Define dispatch and handoff metadata.
4. Define approval checkpoints and audit hooks.
5. Allow multiple implementations while preserving one contract.

3. Non-Goals

1. Forcing all agent products to use one codebase.
2. Standardizing UI/UX across all agent surfaces.
3. Replacing product-specific orchestration logic.

---

4. Core Entities

The shared runtime contract should define:

• AgentSession
• AgentTask
• AgentTodo
• AgentRun
• AgentApprovalCheckpoint
• AgentDispatchRequest
• AgentHandoff
• AgentActionLog

---

5. Minimum Session Shape

type AgentSession = {
  sessionId: string;
  productId: string;
  userId: string;
  status: 'active' | 'paused' | 'waiting-approval' | 'completed' | 'failed' | 'cancelled';
  startedAt: string;
  updatedAt: string;
  resumable: boolean;
  currentTaskId?: string | null;
  memoryRefs: string[];
  artifactRefs: string[];
  approvalRefs: string[];
  dispatchContext?: AgentDispatchContext | null;
};

type AgentTask = {
  taskId: string;
  sessionId: string;
  title: string;
  intent: string;
  status: 'queued' | 'running' | 'blocked' | 'completed' | 'failed' | 'cancelled';
  priority?: string;
  createdAt: string;
  updatedAt: string;
};

type AgentTodo = {
  todoId: string;
  sessionId: string;
  text: string;
  status: 'open' | 'in-progress' | 'done' | 'dropped';
  createdAt: string;
  updatedAt: string;
};

---

6. Required Runtime Behaviors

Every compliant implementation should support:

1. session creation
2. resumable state checkpoints
3. todo/task updates during execution
4. approval checkpoints
5. action-log emission
6. artifact emission
7. dispatch metadata when execution originates elsewhere
8. replayability in ActionTrail

---

7. Dispatch Model

The contract should support:

• browser-originated requests
• mobile-originated requests
• desktop-originated requests
• inter-product dispatch
• trusted desktop executor dispatch

Example:

type AgentDispatchContext = {
  originSurface: 'browser' | 'mobile' | 'desktop' | 'web' | 'product-api';
  originProductId: string;
  dispatchMode: 'interactive' | 'queued' | 'scheduled' | 'remote';
  initiatedAt: string;
};

---

8. First Implementations

The first conforming runtime integrations should target:

1. oss/learning_ai_claw-cowork
2. learning_ai_trails
3. learning_ai_flowmonk
4. learning_ai_jarvis_jr

Later:

5. learning_voice_ai_agent transformation workflows
6. shared operator tools in learning_ai_common_plat

---

9. Key Open Decisions

1. How much of claw-code todo/session semantics should be adopted directly vs normalized?
2. Should scheduled runs create new sessions or new runs under one session?
3. What is the minimum checkpoint payload required for resume-anywhere?
4. Which runtime actions must always emit ActionTrail logs?
5. How should worktree-isolated code tasks be represented vs non-code tasks?

---

10. Acceptance Criteria

1. A dispatched Cowork task can be resumed after interruption without losing audit continuity.
2. A FlowMonk execution can emit task/todo state using the same contract.
3. ActionTrail can replay a run using the shared action-log structure.
4. Approval checkpoints can be handed off to Auth App without losing run context.
5. Product-specific runtimes can remain different internally while still producing the same contract externally.

---

11. Implementation Checklist

• finalize entity list and minimum required fields
• define run vs session vs task boundaries
• define checkpoint/resume semantics
• define dispatch payload contract
• define action-log hook points
• define ActionTrail replay requirements
• define first conforming implementation plan for Cowork and FlowMonk

Commits:

• eae3409 drafted the initial stub
• implementation commits: pending