Master the 5 Core Concepts of AI Agent Orchestration

Agent

Definition

Agent is an AI program that can autonomously execute tasks.

Key capabilities

Perception : read files, understand context, fetch external information.

Planning : decompose complex tasks, devise execution steps, adjust strategies.

Action : write files, run commands, invoke tools.

Reflection : verify results, detect errors, self‑correct.

Typical programming agents

Codex (OpenAI) – code‑generation specialist.

Claude Code (Anthropic) – general programming tasks.

OpenCode (open source) – lightweight programming assistant.

Pi (individual developer) – fast code understanding.

Example interaction

Traditional AI:
You: How do I write a Python function for Fibonacci?
AI: Here is the code... (conversation ends)

AI Agent:
You: Create a Python module that computes Fibonacci and includes tests.
AI:
  → create file fibonacci.py
  → write the function
  → write test_fibonacci.py
  → run tests
  → report results (task completed)

Harness (framework)

Definition

Harness is a software framework that controls, manages, and guides the execution of AI agents.

Core functions

┌───────────────────────────────┐
│          Harness Framework      │
├───────────────────────────────┤
│ 1. Session Management           │
│    - create/destroy sessions    │
│    - maintain context          │
│    - track state               │
├───────────────────────────────┤
│ 2. Permission Control           │
│    - readable files            │
│    - writable directories       │
│    - executable commands       │
├───────────────────────────────┤
│ 3. Tool Integration             │
│    - file‑system access        │
│    - command‑line execution    │
│    - API calls                │
├───────────────────────────────┤
│ 4. Security Mechanisms          │
│    - operation audit           │
│    - risk detection            │
│    - emergency stop            │
└───────────────────────────────┘

Safety rationale

❌ Without Harness:
AI: "I want to modify system files"
User: ... (no control)

✅ With Harness:
AI: "I want to modify system files"
Harness: "High‑risk operation detected, blocked. User notified."
User: Receives alert, reviews, decides.

OpenClaw implementation

OpenClaw Harness Core Components:

┌──────────────────────────────────────┐
│          OpenClaw Gateway             │
│  - session management core            │
│  - tool scheduling hub                │
└──────────────────┬───────────────────┘
                   │
          ┌────────┼────────┐
          ▼        ▼        ▼
   ┌────────┐ ┌────────┐ ┌────────┐
   │  ACP   │ │ Sub‑   │ │  Tool  │
   │Runtime│ │ agents │ │ Layer │
   └────────┘ └────────┘ └────────┘

Protocol

Definition

Protocol is the set of language rules governing communication between AI agents.

ACP (Agent Client Protocol)

ACP is an open, standardized AI‑agent communication protocol.

Core functions of ACP

Standardized interface : plug‑and‑play for any ACP‑compatible tool.

Session isolation : each agent runs in an independent session.

State management : supports persistent and one‑shot sessions.

Permission control : fine‑grained tool‑call permissions.

Message example

{
  "type": "task",
  "content": "Create user‑login API",
  "context": {...},
  "permissions": {...}
}

Session

Definition

Session is the conversation context between a user and an AI.

Session types

One‑shot : disposable, no context retention, suited for simple tasks.

Persistent : long‑lived, retains full context, suited for complex projects.

Thread‑bound : bound to a specific chat thread (e.g., Discord/Telegram), routes all messages to the same agent session.

Lifecycle

Create → Active → Idle → Archive/Destroy

1. Create (spawn)
   /acp spawn codex --mode persistent
2. Active – user sends task, AI executes
3. Idle – no activity for a period
4. Archive (keep context) or Destroy (release resources)

Warning: Persistent sessions consume resources; clean up unused sessions regularly.

Orchestration

Definition

Orchestration is directing multiple AI agents to work together.

Three orchestration modes

Main agent + Harness – a single complex task handled by one agent.

Parallel multi‑agent – multiple agents (e.g., Codex, Claude, Test) run concurrently and their results are integrated.

Multi‑layer orchestration – a coordinator agent delegates to sub‑orchestrators (e.g., backend and frontend groups) which in turn manage specialized agents.

Key challenges

Task decomposition : break a vague goal such as “build a blog system” into concrete steps (project structure, database models, backend APIs, frontend pages, tests, deployment).

Context propagation : share a project directory and use persistent sessions so all agents see the same state; the main agent aggregates status.

Result integration : define a standard output format, let the main agent merge results, and involve human review when conflicts arise.

Complete example: User‑login API

Step 1: Create a persistent session
   /acp spawn codex --mode persistent --label "login-api"

Step 2: Send task via ACP
{
  "type": "task",
  "content": "Create user‑login API",
  "session": "login-api"
}

Step 3: Agent executes under OpenClaw Harness
   → create file user_login.py
   → write login function
   → write tests
   → report completion

Step 4 (optional orchestration)
   Main agent schedules:
     • Codex → write API
     • Claude → write documentation
     • Test agent → write tests