deploy: bd50dfa

Author: github-actions[bot]

2.0/llms-full.txt

@@ -48,6 +48,9 @@ The sample app also exposes `/mcp/workflows` for AI clients. Start with
 `get_workflow_history` to operate on v2 workflow instances through structured
 JSON instead of scraping UI text.
 
+For agent-facing docs, MCP, CLI, and diagnostic handles, see
+[AI-assisted development](/docs/2.0/ai-assisted-development).
+
 Not using Laravel? See the [standalone server](/docs/2.0/polyglot/server), [CLI](/docs/2.0/polyglot/cli), or [Python SDK](/docs/2.0/polyglot/python).
 
 # Installation
@@ -5433,6 +5436,10 @@ That's it! You can now create and test workflows.
 
 The sample app also exposes a Laravel MCP server at `/mcp/workflows`. This is the reference AI-client surface for Durable Workflow v2: it gives agents structured workflow discovery, start, status, output, recent typed history, and failure facts without requiring them to scrape Waterline.
 
+For the broader AI-assisted development contract, including v2 LLM manifests,
+CLI exit codes, Waterline exports, and SDK references, see
+[AI-assisted development](/docs/2.0/ai-assisted-development).
+
 The MCP server is registered from `routes/ai.php` by the Laravel MCP package. The exposed workflow keys live in `config/workflow_mcp.php`; each entry can include the workflow class plus discovery metadata such as a description, credential requirements, and expected arguments.
 
 Default tools:
@@ -5455,6 +5462,90 @@ A typical agent loop is:
 
 Use `simple` or `elapsed` for no-credential smoke tests. The `prism` workflow is intentionally exposed as an AI example, but it requires `OPENAI_API_KEY` before a worker can complete it.
 
+# AI-Assisted Development
+
+Durable Workflow v2 is designed so a person can learn the durable execution
+model, then let tools handle the repeated inspection and operation work. The
+important product surface is not a chat prompt; it is the set of stable handles
+an agent can read, call, and cite without guessing.
+
+## Retrieval Surfaces
+
+Use the versioned v2 LLM artifacts when asking an AI assistant about the
+unreleased v2 protocol:
+
+| Surface | URL | Use |
+| --- | --- | --- |
+| v2 manifest | `https://durable-workflow.com/llms-2.0.txt` | Lightweight index of v2 docs source files. |
+| v2 full bundle | `https://durable-workflow.com/llms-full-2.0.txt` | Single-file v2 docs bundle for retrieval. |
+| canonical manifest | `https://durable-workflow.com/llms.txt` | Current stable docs. This remains 1.x until v2 is released. |
+| canonical full bundle | `https://durable-workflow.com/llms-full.txt` | Current stable full bundle. This remains 1.x until v2 is released. |
+
+Keep the version explicit in agent instructions. During the v2 prerelease
+period, ask for the `2.0` docs or provide the v2 manifest URL directly.
+
+## Local MCP Surface
+
+The [sample app](/docs/2.0/sample-app) exposes a Laravel MCP server at
+`/mcp/workflows`. It is the reference AI-client integration for local v2
+workflow development.
+
+The MCP server gives an agent structured workflow operations instead of UI
+scraping:
+
+| Tool | Stable handle |
+| --- | --- |
+| `list_workflows` | Discover configured workflow keys, credential requirements, status values, and recent runs. |
+| `start_workflow` | Start a configured v2 workflow and receive `workflow_id`, `run_id`, status, business key, and command outcome. |
+| `get_workflow_result` | Poll the current or selected run for status, output, visibility metadata, and latest failure summary. |
+| `get_workflow_history` | Fetch a bounded tail of typed v2 history events and recent durable failures. |
+
+Use `simple` or `elapsed` as no-credential smoke tests. Use workflows with
+external credentials only after the local environment contains the required
+keys.
+
+## Command And Diagnostic Contracts
+
+Agents should prefer machine-readable surfaces over screenshots or prose:
+
+- `dw` commands expose stable exit codes for automation. See the
+  [CLI reference](/docs/2.0/polyglot/cli#exit-codes).
+- Server health, info, namespace, workflow, schedule, worker, and task-queue
+  commands are the right handles for shell-based checks.
+- Waterline history export is the source for replay and failure diagnosis. It
+  includes typed history events, projection source metadata, integrity checks,
+  selected-run context, waits, timers, lineage, and latest durable failures.
+- Python SDK types and method signatures live in the generated
+  [Python API reference](https://python.durable-workflow.com/).
+
+When an agent needs to explain a stuck workflow, collect these facts first:
+
+1. `workflow_id` and `run_id`
+2. current run status and latest failure summary
+3. recent typed history events
+4. open waits, timers, and pending tasks
+5. worker and task-queue health
+6. the relevant docs version, usually `2.0` during prerelease work
+
+That set is enough to distinguish a durable workflow failure from a worker
+runtime failure, a missing external credential, a queue outage, or an operator
+action waiting for approval.
+
+## Prompt Shape
+
+Give coding agents explicit constraints and handles:
+
+```text
+Use Durable Workflow v2 docs from https://durable-workflow.com/llms-full-2.0.txt.
+Use the sample app MCP endpoint at /mcp/workflows for workflow discovery,
+start, result, and history. Prefer dw JSON/exit-code contracts and Waterline
+history exports over screenshots. Do not use 1.x Workflow\Workflow examples
+unless I ask for the stable 1.x API.
+```
+
+The goal is simple: humans learn the workflow/activity/replay invariant, and
+tools operate through documented contracts instead of inferred product behavior.
+
 # Testing
 
 ## `Workflow\V2`

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@@ -13,13 +13,13 @@
 
 
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