docs: add agent-loop.md explaining the tool-use loop and completion signals

docs/features/agent-loop.md

@@ -0,0 +1,188 @@
+# The Agent Loop
+
+How the Copilot CLI processes a user message end-to-end: from prompt to `session.idle`.
+
+## Architecture
+
+```mermaid
+graph LR
+    App["Your App"] -->|send prompt| SDK["SDK Session"]
+    SDK -->|JSON-RPC| CLI["Copilot CLI"]
+    CLI -->|API calls| LLM["LLM"]
+    LLM -->|response| CLI
+    CLI -->|events| SDK
+    SDK -->|events| App
+```
+
+The **SDK** is a transport layer — it sends your prompt to the **Copilot CLI** over JSON-RPC and surfaces events back to your app. The **CLI** is the orchestrator that runs the agentic tool-use loop, making one or more LLM API calls until the task is done.
+
+## The Tool-Use Loop
+
+When you call `session.send({ prompt })`, the CLI enters a loop:
+
+```mermaid
+flowchart TD
+    A["User prompt"] --> B["LLM API call\n(= one turn)"]
+    B --> C{"toolRequests\nin response?"}
+    C -->|Yes| D["Execute tools\nCollect results"]
+    D -->|"Results fed back\nas next turn input"| B
+    C -->|No| E["Final text\nresponse"]
+    E --> F(["session.idle"])
+
+    style B fill:#1a1a2e,stroke:#58a6ff,color:#c9d1d9
+    style D fill:#1a1a2e,stroke:#3fb950,color:#c9d1d9
+    style F fill:#0d1117,stroke:#f0883e,color:#f0883e
+```
+
+The model sees the **full conversation history** on each call — system prompt, user message, and all prior tool calls and results.
+
+**Key insight:** Each iteration of this loop is exactly one LLM API call, visible as one `assistant.turn_start` / `assistant.turn_end` pair in the event log. There are no hidden calls.
+
+## Turns — What They Are
+
+A **turn** is a single LLM API call and its consequences:
+
+1. The CLI sends the conversation history to the LLM
+2. The LLM responds (possibly with tool requests)
+3. If tools were requested, the CLI executes them
+4. `assistant.turn_end` is emitted
+
+A single user message typically results in **multiple turns**. For example, a question like "how does X work in this codebase?" might produce:
+
+| Turn | What the model does | toolRequests? |
+|------|-------------------|---------------|
+| 1 | Calls `grep` and `glob` to search the codebase | ✅ Yes |
+| 2 | Reads specific files based on search results | ✅ Yes |
+| 3 | Reads more files for deeper context | ✅ Yes |
+| 4 | Produces the final text answer | ❌ No → loop ends |
+
+The model decides on each turn whether to request more tools or produce a final answer. Each call sees the **full accumulated context** (all prior tool calls and results), so it can make an informed decision about whether it has enough information.
+
+## Event Flow for a Multi-Turn Interaction
+
+```mermaid
+flowchart TD
+    send["session.send({ prompt: "Fix the bug in auth.ts" })"]
+
+    subgraph Turn1 ["Turn 1"]
+        t1s["assistant.turn_start"]
+        t1m["assistant.message (toolRequests)"]
+        t1ts["tool.execution_start (read_file)"]
+        t1tc["tool.execution_complete"]
+        t1e["assistant.turn_end"]
+        t1s --> t1m --> t1ts --> t1tc --> t1e
+    end
+
+    subgraph Turn2 ["Turn 2 — auto-triggered by CLI"]
+        t2s["assistant.turn_start"]
+        t2m["assistant.message (toolRequests)"]
+        t2ts["tool.execution_start (edit_file)"]
+        t2tc["tool.execution_complete"]
+        t2e["assistant.turn_end"]
+        t2s --> t2m --> t2ts --> t2tc --> t2e
+    end
+
+    subgraph Turn3 ["Turn 3"]
+        t3s["assistant.turn_start"]
+        t3m["assistant.message (no toolRequests)\n"Done, here's what I changed""]
+        t3e["assistant.turn_end"]
+        t3s --> t3m --> t3e
+    end
+
+    idle(["session.idle — ready for next message"])
+
+    send --> Turn1 --> Turn2 --> Turn3 --> idle
+```
+
+## Who Triggers Each Turn?
+
+| Actor | Responsibility |
+|-------|---------------|
+| **Your app** | Sends the initial prompt via `session.send()` |
+| **Copilot CLI** | Runs the tool-use loop — executes tools and feeds results back to the LLM for the next turn |
+| **LLM** | Decides whether to request tools (continue looping) or produce a final response (stop) |
+| **SDK** | Passes events through; does not control the loop |
+
+The CLI is purely mechanical: "model asked for tools → execute → call model again." The **model** is the decision-maker for when to stop.
+
+## `session.idle` vs `session.task_complete`
+
+These are two different completion signals with very different guarantees:
+
+### `session.idle`
+
+- **Always emitted** when the tool-use loop ends
+- **Ephemeral** — not persisted to disk, not replayed on session resume
+- Means: "the agent has stopped processing and is ready for the next message"
+- **Use this** as your reliable "done" signal
+
+The SDK's `sendAndWait()` method waits for this event:
+
+```typescript
+// Blocks until session.idle fires
+const response = await session.sendAndWait({ prompt: "Fix the bug" });
+```
+
+### `session.task_complete`
+
+- **Optionally emitted** — requires the model to explicitly signal it
+- **Persisted** — saved to the session event log on disk
+- Means: "the agent considers the overall task fulfilled"
+- Carries an optional `summary` field
+
+```typescript
+session.on("session.task_complete", (event) => {
+    console.log("Task done:", event.data.summary);
+});
+```
+
+### Autopilot mode: the CLI nudges for `task_complete`
+
+In **autopilot mode** (headless/autonomous operation), the CLI actively tracks whether the model has called `task_complete`. If the tool-use loop ends without it, the CLI injects a synthetic user message nudging the model:
+
+> *"You have not yet marked the task as complete using the task_complete tool. If you were planning, stop planning and start implementing. You aren't done until you have fully completed the task."*
+
+This effectively restarts the tool-use loop — the model sees the nudge as a new user message and continues working. The nudge also instructs the model **not** to call `task_complete` prematurely:
+
+- Don't call it if you have open questions — make decisions and keep working
+- Don't call it if you hit an error — try to resolve it
+- Don't call it if there are remaining steps — complete them first
+
+This creates a **two-level completion mechanism** in autopilot:
+1. The model calls `task_complete` with a summary → CLI emits `session.task_complete` → done
+2. The model stops without calling it → CLI nudges → model continues or calls `task_complete`
+
+### Why `task_complete` might not appear
+
+In **interactive mode** (normal chat), the CLI does not nudge for `task_complete`. The model may skip it entirely. Common reasons:
+
+- **Conversational Q&A**: The model answers a question and simply stops — there's no discrete "task" to complete
+- **Model discretion**: The model produces a final text response without calling the task-complete signal
+- **Interrupted sessions**: The session ends before the model reaches a completion point
+
+The CLI emits `session.idle` regardless, because it's a mechanical signal (the loop ended), not a semantic one (the model thinks it's done).
+
+### Which should you use?
+
+| Use case | Signal |
+|----------|--------|
+| "Wait for the agent to finish processing" | `session.idle` ✅ |
+| "Know when a coding task is done" | `session.task_complete` (best-effort) |
+| "Timeout/error handling" | `session.idle` + `session.error` ✅ |
+
+## Counting LLM Calls
+
+The number of `assistant.turn_start` / `assistant.turn_end` pairs in the event log equals the total number of LLM API calls made. There are no hidden calls for planning, evaluation, or completion checking.
+
+To inspect turn count for a session:
+
+```bash
+# Count turns in a session's event log
+grep -c "assistant.turn_start" ~/.copilot/session-state/<sessionId>/events.jsonl
+```
+
+## Further Reading
+
+- [Streaming Events Reference](./streaming-events.md) — Full field-level reference for every event type
+- [Session Persistence](./session-persistence.md) — How sessions are saved and resumed
+- [Hooks](./hooks.md) — Intercepting events in the loop (permissions, tools)

docs/features/index.md

@@ -8,6 +8,7 @@ These guides cover the capabilities you can add to your Copilot SDK application.
 
 | Feature | Description |
 |---|---|
+| [The Agent Loop](./agent-loop.md) | How the CLI processes a prompt — the tool-use loop, turns, and completion signals |
 | [Hooks](./hooks.md) | Intercept and customize session behavior — control tool execution, transform results, handle errors |
 | [Custom Agents](./custom-agents.md) | Define specialized sub-agents with scoped tools and instructions |
 | [MCP Servers](./mcp.md) | Integrate Model Context Protocol servers for external tool access |