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The three-agent chain from the previous chapter worked because the planner figured the plan out from scratch. That’s fine once - but let’s say your team keeps asking the same class of question:
“Research this, compute some percentage of it, write a poem about the result.”
Every plan the planner re-derives the same steps. You pay for the LLM time every time. What if you could write the plan down once, in plain markdown, and have the planner load it on demand when it recognizes a match? That’s a recipe.

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The core idea: progressive disclosure

You could try solving this by dumping a big “how to coordinate these agents” paragraph into the planner’s system prompt. Fine for one pattern. Doesn’t scale - after 20 patterns, your system prompt is 20,000 tokens and the planner is paying to read it all on every request, even the ones that don’t need any of them.
Recipes fix this with a technique called progressive disclosure. At every turn the planner sees:
  • The name and one-line description of every recipe - cheap, a few hundred tokens even for dozens of recipes.
  • A tool called load_recipe({name}) in its toolbox.
Only when the planner recognizes a match does it call load_recipe. The tool’s reply is the full recipe body - typically a 2–3 KB markdown playbook - injected into the conversation. The planner then follows the body for the rest of the turn.
You paid for the body’s tokens exactly once per plan, and only when the recipe was actually relevant.

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Your first recipe

Let’s write one. Create a file at gateway/recipes/research-math-poem/RECIPE.md:
gateway/recipes/research-math-poem/RECIPE.md
---
name: research-math-poem
description: Research a factual number, compute a percentage of it, and write a short poem about the result. Load when the user asks a three-part question combining research, arithmetic, and creative writing.
tags: [research, math, creative]
triggers: [research and compute, percentage poem, population percent]
---

# Recipe: research-math-poem

Use this when the user's question has three distinct phases:

  1. A factual lookup (population, revenue, distance, etc.)
  2. A percentage or fraction applied to that number
  3. A short creative response about the result

## Flow

1. **Research.** Call `call_research_web_research` with the user's exact
   factual question. Don't translate or summarize it.
2. **Extract the number.** In your own reasoning (not as a tool call),
   pull the headline figure from the research reply. Prefer the
   *headline* number the user asked about, not incidental figures.
3. **Compute.** Call `call_math_solve` with the computation stated
   explicitly: "Compute 0.5% of 36,950,000". Don't ask the math agent
   to interpret - give it the exact expression.
4. **Create.** Call `call_poet_write_poem` with the computed number
   and the user's creative framing (line count, mood, subject).
5. **Respond.** Write a final message that shows all three steps
   briefly and ends with the poem.

## Constraints

- **Do not parallelize** the calls. The math depends on the research;
  the poem depends on the math.
- **Do not invent the number** if research returns ambiguous output.
  Ask the user to clarify which population/revenue/etc. they mean.
- **Do not skip the poem** if the user asked for one. If
  `call_poet_write_poem` fails, surface the failure; don't silently
  produce prose.

​
Watching it load

Restart the gateway (Ctrl+C in its terminal, npm run dev again). The loader scans gateway/recipes/ once at boot and caches the result — there is no per-request reload. To confirm yours was picked up, hit /health: 
curl -s http://localhost:3774/health | jq '.runtime.recipe_count'

3
Three because two recipes ship with the gateway by default — multi-agent-research and payment-required-flow (see gateway/recipes/) — plus your new one. If the count didn’t go up, double-check the file path and frontmatter.
Now fire the same three-agent question from the previous chapter. In the SSE stream you should see an extra event early on: 
event: task.started
data: {"task_id":"call_xyz...","agent":"load_recipe","skill":"","input":{"name":"research-math-poem"}}

event: task.artifact
data: {"task_id":"call_xyz...","content":"<recipe_content name=\"research-math-poem\">\n# Recipe: research-math-poem\n\nUse this when the user's question has three distinct phases: ...</recipe_content>"}

event: task.finished
data: {"task_id":"call_xyz...","state":"completed"}
The planner recognized the match, called load_recipe, and now has your playbook in context. The rest of the plan - research, math, poet - follows the recipe.

​
Does it actually change behavior?

Sometimes yes, sometimes no. The planner was already good at this class of question; the recipe mostly pins the behavior (forces the specific tool order, specific call shapes) rather than enabling something new. Where recipes shine:

Edge-case handling

A recipe that says “if you see state: payment-required, surface the payment URL to the user and STOP - do not retry” is a policy the planner wouldn’t invent on its own.

Tenant-specific rules

A recipe visible only to a certain agent can encode rules like “always include a disclaimer” or “always call the compliance agent first.”

Multi-hop orchestration

A recipe describing a 5-step workflow is a document your team can review, version, and reason about. Inline planner reasoning isn’t.
See gateway/recipes/payment-required-flow/RECIPE.md in the repo for a real-world example of edge-case handling.

​
Recipe layouts

Two supported shapes: 
gateway/recipes/foo.md
A single markdown file. No bundled siblings. Good for short playbooks.
When the planner loads a bundled recipe, the load_recipe tool result includes a <recipe_files> listing of the sibling files (capped at 10 for token sanity). The planner can refer to them by relative path in its response or follow instructions in the body like “run scripts/validate.sh before responding.”

​
Frontmatter reference

---
name: unique-identifier          # required; cannot start with "call_"
description: one-line summary    # required (non-empty) - this is the hook
tags: [tag1, tag2]               # optional; surfaced in verbose listings
triggers: [phrase, phrase]       # optional; planner hints (not enforced)
---
Two rules the loader enforces:
Duplicate recipe names cause boot to fail with a clear error - silent precedence would make behavior depend on filesystem order.
Planner tool ids look like call_agent_skill; a recipe named call_anything would visually collide in the load_recipe tool description. Rejected at load time.

​
Per-agent recipe visibility

The gateway’s agent configs (in gateway/agents/*.md) have a permission: block. You can use it to scope recipes:
gateway/agents/planner.md
permission:
  recipe:
    "internal-*": "deny"      # this agent can't load recipes matching "internal-*"
    "*": "allow"              # everything else is fine
The planner only sees (and can only load) recipes matching its allowed patterns. Default is allow - agents with no recipe: rules see everything.

​
The full authoring loop

1

Create the recipe file.

Either gateway/recipes/<name>.md or gateway/recipes/<name>/RECIPE.md.
2

Restart the gateway.

The loader scans on boot (no hot reload yet).
3

Fire a /plan request that should trigger the recipe.

Use the exact phrasing your users will use.
4

Read the SSE stream for a load_recipe tool call.

That’s the planner saying “I recognized this pattern.”
5

If the planner didn't load it - tighten the description.

That’s what the planner reads. Add specific keywords the user question likely contains.
Recipes are the single highest-leverage operator tool in the gateway. Spend an afternoon writing five for your common question shapes and you’ll notice your planner’s behavior firming up across the board.
Next: give the gateway a cryptographic identity and start signing outbound calls. DID signing → Sunflower LogoWrite the pattern once - then planner loads it when it’s needed.