agentbuilder

name

agentbuilder

description

Use this skill when creating, editing, debugging, reviewing, or documenting Standard Agents or AgentBuilder projects. Apply it for work on agents, prompts, models, tools, hooks, threads, APIs, subagents, provider setup, model selection, environment variables, and spec-aligned architecture, even if the user only says things like "build an agent", "write a prompt", "choose a model", or "fix my AgentBuilder app" without explicitly naming Standard Agents.

AgentBuilder Skill

This guide is a living record. It is not part of the Standard Agent Specification — it exists to help humans and coding agents use the spec to build effective agents. It is updated regularly and not bound to any spec version.

Commands like pnpm exec agents … are placeholders. Use whichever package manager (npm, pnpm, yarn, bun) the project actually uses.

For deeper reference material that this skill links to, see:

• agents/agents/AGENTS.md — agent definition reference (created by agents scaffold)
• agents/prompts/AGENTS.md — prompt and tool config reference
• agents/tools/AGENTS.md — tool-writing patterns and ThreadState examples
• agents/models/AGENTS.md — recommended model list (authoritative)
• agents/hooks/AGENTS.md — hook reference
• Canonical TypeScript types (full signatures): read node_modules/@standardagents/spec/dist/ in your project, or browse packages/spec/src/ on GitHub at https://github.com/standardagents/agentbuilder
• Specification: https://standardagentspec.org/llms.txt
• Builder docs: https://docs.standardagentbuilder.com/llms.txt

If the agents/*/AGENTS.md files don't exist in your project yet, run pnpm exec agents scaffold to create them.

---

Before you write any code

Most failed Standard Agent projects fail in the same six ways. Read this section first and answer all six questions explicitly before creating any code. Each links to the deeper section that explains how.

1. Architect the agent graph before picking types. List the domains the system touches. Determine the tree (coordinator → domain agents → sub-domain agents). Then decide what each node is. Do not start by writing one agent and bolting tools onto it. → Architecture & decomposition

2. dual_ai is the default. ai_human is the exception. ai_human is correct only when the thread itself is the chat surface (chat UI, website widget, AgentBuilder admin, direct API chat). For Slack, email, SMS, Discord, webhooks, polled inboxes, schedulers, or any other mediated channel, the human is just a tool target — the agent is dual_ai. The whole graph being dual_ai is fine and often cleaner. → Interaction type

3. Every dual_ai agent must have explicit session boundaries. Name its sessionStop tool. Name its sessionFail tool. Set a finite maxSessionTurns. Give side_b a real, non-redundant job. If you cannot say in one sentence what tool call ends the session, the agent is not designed yet. → Session boundary discipline

4. Pick models from current-models only. Never invent model strings from memory. Run pnpm exec agents current-models, choose by category, then run pnpm exec agents available-models --provider=<name> to confirm the exact string. → Model selection

5. Research every third-party API before writing the tool. Fetch the official current docs. Confirm auth, base URL, endpoint paths, payload shapes, rate limits, error codes. Do not write a tool from memory of how an API "usually" works. APIs change. → API research checklist

6. Check ThreadState before adding any dependency. Before reaching for S3, Redis, an external cron, a queue service, or even node:fs, confirm the framework does not already provide the capability via ThreadState. It almost always does. → ThreadState first

---

What is a Standard Agent?

In the Standard Agent paradigm, agents are the atomic unit of an AI system, and it is the composition of many domain-specific agents that produces efficacy. Standard Agents can be effective using small and cheap models — but small and cheap models suffer from poor tool discernment when presented with a broad, undifferentiated set of tools. Decomposition is what makes them work.

A "gmail" agent will outperform a "google apps" agent. A higher-level "communications" coordinator composes the gmail, slack, and SMS agents. A still-higher "personal assistant" coordinator composes communications, research, scheduling, and finance. This is fractal: the same shape scales up to teams, departments, and entire products. These agent-graphs solve real industry problems — progressive tool discovery, model/prompt tuning, context dilution, task resumability, and compaction.

Just because subagents can compose complex behavior doesn't mean every step needs one. A subagent is a two-sided conversation where each side may take multiple steps per turn. Sometimes you only need a feature of a different model — generating an image, rewriting a paragraph in a more eloquent voice. For those, use a subprompt (a single LLM step exposed as a tool), not a subagent. Use a subagent when you need iteration, QA, reflection, or long-lived addressable behavior.

When a subagent does QA on another model's output, prefer a different provider on the reviewing side. Same-lab models tend to rate their own output too generously.

Example graph

personal_assistant_coordinator         (dual_ai, top-level reasoning model)
├── communications_coordinator         (dual_ai, resumable, explicit parent communication, note: children are explicit because they receive inbound messages and should filter out noise before returning)
│   ├── gmail_agent                    (dual_ai, resumable, immediate, explicit parent communication)
│   ├── slack_agent                    (dual_ai, resumable, immediate, explicit parent communication)
│   └── sms_agent                      (dual_ai, resumable, immediate, explicit parent communication)
├── research_assistant                 (dual_ai, resumable, explicit parent communication)
│   ├── browser_use_agent              (dual_ai, resumable, implicit parent communication)
│   ├── google_drive_agent             (dual_ai, resumable, implicit parent communication)
│   └── notes_agent                    (dual_ai, resumable, implicit parent communication)
├── scheduling_coordinator             (subprompt)
└── finance_ops_coordinator            (dual_ai)

Note: no ai_human anywhere. The "human" enters via the gmail/slack/sms tool calls. The whole graph is autonomous.

The Standard Agent stack

The spec defines an agent as a composition of:

• Providers — how to talk to LLM providers and model variants
• Models — named model definitions referencing a provider
• Prompts — system instructions plus the tools, subprompts, and subagents available at that step
• Tools — functions the agent can call to interact with the world (and other agents)
• Agents — bind it all together: name, type, sides, session bindings
• Hooks — custom code that intercepts lifecycle events (history filtering, message injection, tool result transforms)
• Effects — custom code scheduled to run later
• Threads — runtime instances of an agent, each with its own state, message history, and filesystem
• Endpoints — built-in and custom HTTP endpoints exposed by a thread

---

Architecture & decomposition

Procedure (do this in order, every time):

1. List the domains the system actually touches.
2. Draw the tree on paper or in a comment block. Coordinators on top, leaf agents at the bottom.
3. Pick interaction types. (Default dual_ai. Use ai_human only at chat-surface entry points.)
4. Name session boundaries for every dual_ai node.
5. Pick a model category for every node.
6. List the third-party APIs each leaf needs and queue them for research.
7. Map each capability to a ThreadState primitive before writing any custom plumbing.

Only then start writing files.

Mega-agent smell test

If a single agent has more than ~8 tools across unrelated domains, decompose it. Decomposition is rarely wrong; flattening usually is.

Other smells that mean "decompose now":

• One prompt has tools from two clearly different worlds (e.g., send_email and query_postgres and generate_image).
• The system prompt is trying to teach the model when to use which tool by writing rules in English. Rules-in-prose are coordinator logic; promote them to a coordinator that picks subagents.
• A model keeps calling the wrong tool because two tools have similar names or overlapping descriptions. Different domains, different agents.
• The agent's prompt is over ~150 lines. That's almost always context dilution — split.

Worked example: wrong vs. right

Wrong — one mega-agent with tools from unrelated domains:

personal_assistant (ai_human)
  tools: send_gmail, read_gmail, search_ebay_listings, place_ebay_order,
         track_ebay_shipment, create_calendar_event, list_calendar_events,
         post_slack_message, read_slack_channel, query_stock_price,
         execute_stock_trade, search_recipes, generate_image

Gmail, eBay, calendar, Slack, stock trading, recipes, image gen — seven unrelated worlds in one tool list. The model picks send_gmail when it meant post_slack_message. It calls execute_stock_trade with eBay listing IDs. The system prompt grows 80 lines of rules trying to teach it which tool belongs to which world. Every bug fix breaks two unrelated flows.

Right — coordinator + domain subagents:

personal_assistant_coordinator (dual_ai, reasoning model)
├── gmail_agent        tools: send_gmail, read_gmail
├── ebay_agent         tools: search_ebay_listings, place_ebay_order,
│                             track_ebay_shipment
├── calendar_agent     tools: create_calendar_event, list_calendar_events
├── slack_agent        tools: post_slack_message, read_slack_channel
├── trading_agent      tools: query_stock_price, execute_stock_trade
└── content_helpers    subprompts: search_recipes, generate_image

personal_assistant_coordinator runs a reasoning model and decides which domain owns the next step. Each leaf is a fast tool-calling model with a tight tool set it can actually discern between. A fix to the eBay flow can't break Gmail.

Coordinators

Coordinators provide two things flat agents can't:

1. Inter-domain communication. A coding_coordinator can ask research_agent to investigate a library, then hand the findings to bash_agent to run commands. The bash agent never needed to know the research agent existed.
2. Filtering. A gmail_agent can filter spam well on its own. But the higher-level objectives of the organization — "what email actually matters to the CEO of the sprinkler hose company" — belong to a coordinator above it. The coordinator filters again before escalating.

Graph depth tradeoffs

Deeper graphs add latency and create more inter-agent communication that can fail. But they also enable parallelism and isolation. A marketing_communications_agent can have a social_media_agent which has twitter_agent, linkedin_agent, and facebook_agent as children. That subtree handles posting workflows entirely without involving the top coordinator. Aim for depth that matches the natural hierarchy of the work, not depth for its own sake.

---

Interaction type: dual_ai is the default

dual_ai is the default agent shape. ai_human is the exception, used only when the thread itself is the chat surface — chat UI, website widget, AgentBuilder admin, or an API the user is talking to directly.

For Slack, email, SMS, Discord, webhooks, polled inboxes, schedulers, or any other mediated channel, the human is just a tool target. The agent on the framework side is dual_ai. The whole graph being dual_ai is fine — often cleaner.

The single decision

Ask: "Is the human typing directly into this thread?"

• Yes → the top-level agent is ai_human.
• No → the top-level agent is dual_ai. The human enters the graph via a tool somewhere (e.g., a send_slack_message call inside a slack subagent).

Do not default to ai_human just because a human is eventually involved. Ask where messages physically arrive.

The two shapes

Shape A — chat surface (ai_human at top):

website_chatbot (ai_human)         ← user types in a chat widget
  tools: search_products, lookup_order, escalate_to_human

Shape B — mediated (dual_ai everywhere):

slack_research_assistant (dual_ai, reasoning model)
├── slack_agent (dual_ai, resumable) ← messages arrive via tool calls,
│                                      not as thread messages
└── research_agent (dual_ai)

In Shape B, side_a of slack_research_assistant plans the work and dispatches subagents. Side_b reviews and decides when the work is done. The "user" is a Slack channel reachable through slack_agent's tools.

Handoffs (a special case of ai_human)

When an ai_human agent calls another ai_human agent as a tool, the runtime does not spawn a new thread — it changes which prompt "owns" the existing thread. This is a handoff. Useful for stepwise human flows: an onboarding_agent collects information, then hands off to a scheduling_agent that books a meeting. The user keeps talking to the same thread.

Subprompts vs. subagents vs. handoffs

• Subprompt — one LLM step exposed as a tool. Use to switch models for a focused task: image generation, polished writing, JSON extraction.
• Subagent — full child thread with its own ThreadState. Use when you need iteration, QA, reflection, or long-lived addressable behavior. Always dual_ai.
• Handoff — ai_human → ai_human, swaps prompt ownership of the same thread. Use for stepwise human-driven flows.

A subagent can be:

• Blocking + non-resumable — the parent waits, the child runs once, returns, and is gone (tool-call style)
• Blocking + resumable — the parent waits but the child remains addressable for future calls
• Non-blocking + non-resumable — fire-and-forget one-shot
• Non-blocking + resumable — long-lived addressable child the parent can re-message later (e.g., a Slack monitor that lives as long as the parent)

---

Session boundary discipline

This section is a hard rule, not a suggestion. Every dual_ai agent in the graph must explicitly define its session boundaries. This is what makes whole-graph dual_ai safe.

Required for every dual_ai agent

• sessionStop tool binding — names the tool call that ends the session with a result. Side_a or side_b invokes it when the work is done. Common patterns: report_findings, submit_to_parent, final_answer, done.
• sessionFail tool binding — names the tool call that ends the session with a failure the parent should see. Common patterns: escalate_blocked, report_unresolvable, give_up_with_reason.
• maxSessionTurns — a finite integer sized to the realistic upper bound for the task. Never omit this. A research subagent might be 20; an asset QA loop might be 6; a tight reflection loop might be 3.
• A real job for side_b — reflection, QA, judging, alternative-perspective driving, devil's advocate. If side_b is "another instance of side_a," you don't have a dual_ai agent — collapse it to a single-side prompt or a coordinator pattern.
• Cross-provider QA — when side_b reviews side_a's output, pick a model from a different provider than side_a. Same-lab models systematically over-rate their own work.

The smell test

If you cannot articulate in one sentence what tool call ends the session, the agent is not designed yet.

Examples that pass:

• "Side_a calls submit_video_assets once side_b approves the renders."
• "Side_b calls report_findings after the research turn count exceeds 5 or it judges the topic exhausted."
• "Either side calls escalate_to_parent if the customer policy question is unresolvable."

Examples that fail:

• "It just stops when it's done." (How does the runtime know?)
• "Whichever side decides." (Decides by calling what?)

Why this matters

A dual_ai agent with no sessionStop, no sessionFail, and no maxSessionTurns will burn turns until it hits a runtime cap, then fail in a way the parent can't interpret. Coding agents that have been bitten by this once will start defaulting to ai_human to "feel safer" — and then we're back to mega-agents and chat-surface confusion. Boundary discipline is what keeps dual_ai the default.

---

Model selection

Rule: never write a model string the user did not request and current-models did not produce.

Required workflow

1. Run pnpm exec agents current-models to see the curated category list (e.g. extra_reasoning, fast_tool_calls, writing, image_generation, tiny).
2. Choose the category that fits the role (table below).
3. Run pnpm exec agents available-models --provider=<name> to confirm the exact provider model string.
4. Define the model in agents/models/<name>.ts using defineModel.

If you have multiple configured providers, pass --provider=<name> explicitly.

Role → category mapping

Role	Category	Notes
Top-level coordinator	extra_reasoning	The discerning brain. Don't skimp here.
Domain subagent (gmail, slack, etc.)	fast_tool_calls	High volume, narrow tool set, latency matters.
Eloquent text generation	writing	Use as a subprompt from a fast-tool-calling agent.
Image generation	image_generation	Use as a subprompt.
QA / reviewer side_b	extra_reasoning from a different provider than side_a	Same-lab QA is biased.
Cheap classification, tagging, routing	tiny	Where speed and cost dominate quality.

Fallback strategy

Define more than one model per category, on different providers. The model name should describe the use case, not the provider, so a fallback can substitute transparently:

agents/models/extra_reasoning.ts          → primary (provider A)
agents/models/extra_reasoning_fallback.ts → secondary (provider B)

Prompts and agents reference extra_reasoning; if the primary provider is down, the fallback is one rename away.

For the authoritative list of which models currently fill each category, see agents/models/AGENTS.md in your project (created by agents scaffold). Do not embed the list here — it drifts.

---

API research checklist

This is the single biggest gap in most coding-agent-built tools. Do not write a defineTool that touches a third-party API from memory. APIs change. Auth flows change. Endpoints get deprecated. Read the current docs every time.

Before writing the tool

1. Fetch the official current docs. Use WebFetch, the read-website skill, or a browser. Confirm:
   • Auth method (bearer, OAuth, signed request, API key in header vs. query)
   • Base URL and current API version
   • Endpoint paths and HTTP methods
   • Request payload shape (and required vs. optional fields)
   • Response payload shape (success and error)
   • Rate limits and Retry-After handling
   • Pagination model (cursor, offset, link header)
   • Idempotency rules — does retrying duplicate side effects?
2. Confirm SDK availability and Workers compatibility. Is there a first-party JS/TS SDK? Does it run in Cloudflare Workers (no Node built-ins, no fs, no native modules, no long-lived sockets)? If not, use fetch directly. Most provider SDKs are not Workers-compatible out of the box.
3. Identify required secrets. Declare them as secret variables, never text. Secrets must never be referenced in prompt text or returned to the model.
4. Map error modes explicitly. Each gets handled, not ignored:
   • 401 / 403 — auth failed. Surface a clear message; do not retry blindly.
   • 404 — missing resource. Often a user-facing error; surface it.
   • 409 — conflict. Often means the operation already happened; check before retrying.
   • 429 — rate limited. Honor Retry-After. Retry with backoff.
   • 5xx — server error. Retry with exponential backoff, finite cap.
   • 4xx (other) — surface to the model so it can correct its arguments.
5. Prototype the raw call in isolation — a single fetch against the real endpoint with a real token. Confirm the response shape as observed, not as documented. Docs lag.
6. Return shapes the model can actually use. Strip noise. Surface IDs, names, statuses, and the fields the next step needs. Don't return the entire 12 KB JSON blob and hope the model picks the right field.

Anti-pattern: "I know the Slack API, I'll just write it." You don't. It changed last quarter. Read the docs.

---

ThreadState first

Rule: Before adding any dependency or external service, check whether ThreadState already provides the capability.

ThreadState is the unified API passed to every callable, hook, and endpoint. It abstracts the runtime — your tool may run on the edge, on a Worker, or on a Node server, and the same state.readFile() call works in all of them. Tools should not import node:fs, read process.env, or assume Node-shaped APIs.

Capability lookup table

What you need	Use this	Don't use
Store a file between turns	state.writeFile / state.readFile	S3, external blob store
Persist small structured data across turns	state.getValue / state.setValue durable KV	External KV, Redis
Persist large or binary data across turns	state.writeFile / state.readFile	External blob store
Trigger work later	state.scheduleEffect	External cron, queue service
Invoke another tool from inside a tool	state.invokeTool / state.queueTool	Re-implementing tool logic inline
Read/write config and secrets	state.env / state.envType / state.setEnv	process.env, .env files
Run isolated deterministic code	state.runCode with explicit bridges	eval, child processes, implicit runtime access
Search files the thread has seen	state.grepFiles / state.findFiles	Reimplementing search
Escalate / report status to the parent	state.notifyParent / state.setStatus	Custom message bus
Load a sibling prompt / agent / model	state.loadPrompt / state.loadAgent / state.loadModel	Duplicating the definition
Inspect or message a child thread	state.children / state.getChildThread	External orchestration
Inject context the model should see	state.injectMessage / state.queueMessage	Stuffing the system prompt
Read the thread's message history	state.getMessages / state.getMessage	Reimplementing storage
Update an existing message	state.updateMessage	Mutating storage directly
Read execution logs	state.getLogs	External observability shim
Emit a runtime event	state.emit	Console logging
Stop the thread	state.terminate	Throwing and hoping

Method cheat sheet

Discoverability reference. For full signatures, read the spec types from node_modules/@standardagents/spec/dist/ (or browse packages/spec/src/ on GitHub). Worked examples live in agents/tools/AGENTS.md.

Identity     threadId, agentId, userId, createdAt, children, terminated
Messages     getMessages, getMessage, injectMessage, queueMessage, updateMessage
Logs         getLogs
Resources    loadModel, loadPrompt, loadAgent,
             getChildThread, getParentThread,
             getPromptNames, getAgentNames, getModelNames
Env          env, envType, setEnv
Parent       notifyParent, setStatus
Tools        queueTool, invokeTool
Effects      scheduleEffect, getScheduledEffects, removeScheduledEffect
Events       emit
Context      context (Record<string, unknown>, in-memory only)
KV           getValue, setValue
Files        writeFile, readFile, readFileStream, statFile, readdirFile,
             unlinkFile, mkdirFile, rmdirFile, getFileStats,
             grepFiles, findFiles, getFileThumbnail
Execution    execution, terminate, runCode

Durable key-value store

Use state.getValue() and state.setValue() for small per-thread durable JSON values such as counters, checkpoints, cursors, tool state, and user preferences. Values survive restarts and are scoped to the current thread.

const count = (await state.getValue<number>('invocation_count')) ?? 0;
await state.setValue('invocation_count', count + 1);

setValue(key, null) and setValue(key, undefined) delete the key. For larger payloads, binary data, user-visible artifacts, or content that should be shared as a file, use state.writeFile() / state.readFile() instead.

Sandboxed code execution

Use state.runCode() for model- or user-authored JavaScript/TypeScript instead of eval or new Function. The sandbox runs in Cloudflare Dynamic Workers, has no implicit thread state, filesystem, network, timers, or host globals, and only receives capabilities you explicitly bridge through imports or globals.

const run = state.runCode(
  `
    import { readFile } from "fs";

    export async function summarize(path: string) {
      const text = await readFile(path);
      return text.slice(0, 200);
    }
  `,
  {
    language: 'typescript',
    execute: { fn: 'summarize', args: ['/notes/input.txt'] },
    imports: {
      fs: {
        readFile: async (path: string) => {
          const file = await state.readFile(path);
          return file ? new TextDecoder().decode(file) : '';
        },
      },
    },
  },
);

const result = await run;

By default, runCode() executes the default export with no args. Use execute: { fn, args } to call a named export or pass arguments; fn: 'default' calls the default export with args. Use modules to provide local relative ES modules. The result is a status object: successful runs return status: 'success', result, logs, reports, and durationMs; failed runs return an error status and an error object. Call run.terminate(reason) from your own timeout budget when needed.

Notes on a few that are easy to misuse

• state.context is in-memory for the current execution. It is not durable across thread restarts. For durable structured state, use state.getValue / state.setValue.
• state.getValue / state.setValue are durable per-thread JSON storage. Use them for small structured state; use files for larger content or artifacts.
• state.env / state.envType / state.setEnv are for runtime configuration and secrets. state.env(name) resolves thread -> account -> instance -> agent -> prompt. state.envType(name) returns 'secret' by default; 'text' means the value may be shown in tool output. state.setEnv(name, value, { type: 'text' | 'secret' }) writes thread-scoped env and propagates to active descendants. Omit type only when preserving the existing type is intentional; new keys default to secret.
• state.runCode runs JavaScript or TypeScript in an isolated Dynamic Worker sandbox. The sandbox does not receive ThreadState, env, files, network, or host globals implicitly. Pass exact capabilities through imports, globals, modules, and execute. It executes exported values/functions; code such as console.log(fibonacci(5)) can log, but it must still export default ... or export a named function/value for the host to receive a result.
• state.scheduleEffect runs a named effect after a delay. It survives restarts. This is your cron, your queue, and your retry timer all in one.
• state.invokeTool vs state.queueTool — invokeTool runs synchronously and returns the result; queueTool schedules the call to run later in the normal tool-call flow. Prefer queueTool when the model should see the result as a regular tool call.
• state.notifyParent — for resumable subagents with parentCommunication: 'explicit', this is the only way the child talks to the parent. Use it sparingly; every notification interrupts the parent.
• File attachments use the path convention /attachments/{filename}.{ext}. Always use this path when passing files between agents — the runtime copies them across thread filesystems automatically.

Sandboxed code env bridge pattern

When building a coding agent that runs user-authored code, do not expose all thread env and do not rely on process.env. Use an explicit allowlist stored in durable KV:

1. A setup tool such as set_code_envs (called set_code_run_envs in the built-in sandboxed coding agent) receives the required env names and stores the allowlist with state.setValue(...).
2. The execution tool reads that allowlist with state.getValue(...).
3. For each allowed name, the execution tool resolves await state.env(name) and await state.envType(name). If a value is missing, it may create an empty thread env entry with state.setEnv(name, '', { type: 'secret' }) so the UI can prompt the user.
4. The execution tool calls state.runCode(source, { imports: { env: { env: allowedValues } }, ... }), exposing only the whitelisted env object.
5. The tool redacts values whose envType is secret from results, reports, logs, and errors. Values marked text may appear.

The prompt for a coding agent must explain this exact interface. Tell the model to call the allowlist setup tool before running code, then import a static object from "env":

import { env } from "env";

const apiKey = env.WEATHER_API_KEY;

Also tell it what not to do: do not read process.env, do not call env() as a function, do not await env values, do not use named env imports, and do not pass env values around as tool arguments.

---

Tools

A "tool" is anything an agent can call. There are three kinds:

1. Callables — TypeScript functions defined via defineTool in agents/tools/. The primary way to interface with the outside world: APIs, databases, business logic, and (sometimes) other agents. Each callable receives a ThreadState. Do not assume Node APIs are available — your code may run on the edge.
2. Subprompts — prompts exposed as tools via their toolDescription. A single-step LLM call. Use for switching models on a focused task (image generation, polished writing, JSON extraction).
3. Subagents — full agents exposed as tools via exposeAsTool: true on the agent definition. Use when you need iteration, QA, reflection, or long-lived addressable behavior. Always dual_ai.

Provider-visible argument schemas

Tool argument schemas are sent to model providers, and strict tool-calling providers commonly reject JSON Schema objects unless every object schema has additionalProperties: false. This applies recursively, not just at the top level. If a tool has nested object args, arrays of objects, anyOf object branches, prompt requiredSchema, or an agent exposed as a tool, verify the model-facing schema contains additionalProperties: false for every type: "object".

Common failure modes:

• z.record(...) can emit propertyNames, which some providers reject for tool schemas. Prefer explicit z.object({ ... }) shapes for provider-visible tool args.
• z.object({}).catchall(...) can emit additionalProperties: {}. Strict providers expect the boolean false, not an object schema.
• "Arbitrary object" tool args are a poor fit for strict provider tool schemas. Prefer a JSON string for truly arbitrary payloads, or define the object properties explicitly.

PromptDefinition cheat sheet

A prompt is what actually gets sent to the LLM at one step. Set on each prompt file in agents/prompts/. For full signatures, read the spec types from node_modules/@standardagents/spec/dist/ (or browse packages/spec/src/ on GitHub), and see agents/prompts/AGENTS.md.

PromptDefinition
  name                   string                          unique snake_case identifier
  toolDescription        string                          shown when this prompt is exposed as a tool
  prompt                 string | PromptContent[]        system prompt (string, or composable parts)
  model                  ModelName                       references agents/models/<name>
  includeChat            boolean (default false)         pass full chat history to this LLM step
  includePastTools       boolean (default false)         pass past tool call results
  parallelToolCalls      boolean (default false)         allow multiple tool calls per turn
  toolChoice             'auto' | 'none' | 'required'    tool calling strategy (default 'auto')
  requiredSchema         ZodSchema                       validate args when called as a tool
  variables              VariableDefinition[]            declared text/secret variables
  tools                  (string | SubpromptConfig | PromptToolConfig | SubagentToolConfig)[]
                                                         tools available at this step
  env                    Record<string, string>          prompt-level env values (lowest precedence)
  reasoning              ReasoningConfig                 extended thinking config (for models that support it)
  recentImageThreshold   number (default 10)             how many recent messages keep real images
  providerOptions        Record<string, unknown>         passthrough to the provider (overrides model defaults)
  hooks                  HookId[]                        prompt-scoped hooks (overrides agent hooks)

Composable prompts: the tone pattern

prompt can be a string or an array of parts. Use parts to compose a shared "tone" or "persona" across many prompts so changes flow through one place.

const tonePrompt: PromptDefinition = {
  name: 'company_tone',
  toolDescription: 'Defines the tone and style of the chatbot.',
  prompt: `You are a friendly and helpful customer support assistant for an athletic shoe company. You always respond in a positive and upbeat tone, even when the customer is upset. You use simple language and avoid technical jargon.`,
  model: 'tiny',
};

const shippingPrompt: PromptDefinition = {
  name: 'shipping_inquiries',
  toolDescription: 'Handles customer questions about shipping.',
  prompt: [
    { type: 'include', prompt: 'company_tone' },
    { type: 'text', content: 'Details about shipping policies: ...' },
  ],
  model: 'tiny',
};

Use { type: 'env', property: 'PRODUCT_INVENTORY' } parts to inject runtime values into the prompt. Combined with variables, this lets a generic agent be specialized per-thread without code changes:

{
  name: 'ecommerce_assistant',
  toolDescription: 'Assistant for ecommerce businesses.',
  prompt: [
    { type: 'text', content: 'You help customers with products and orders. Current inventory: ' },
    { type: 'env', property: 'PRODUCT_INVENTORY' },
  ],
  model: 'tiny',
  variables: [
    {
      name: 'PRODUCT_INVENTORY',
      type: 'text',
      required: true,
      description: 'Full product inventory with names, descriptions, and stock levels.',
    },
  ],
}

AgentDefinition cheat sheet

The agent definition binds prompts and sides together. Set on each file in agents/agents/. For full signatures, see agents/agents/AGENTS.md.

AgentDefinition
  name              string                       unique snake_case identifier
  type              'ai_human' | 'dual_ai'       default 'ai_human' — but see "dual_ai is the default"
  maxSessionTurns   number                       REQUIRED for dual_ai. Finite turn cap.
  sideA             SideConfig                   AI side (or first AI in dual_ai)
  sideB             SideConfig                   second AI side; required for dual_ai
  exposeAsTool      boolean (default false)      enables this agent to be called as a tool by other prompts
  toolDescription   string                       required if exposeAsTool: true
  description       string                       brief human description for UIs
  icon              string                       URL or absolute path
  env               Record<string, string>       agent-level env values
  hooks             HookId[]                     agent-scoped hooks (when prompts don't specify their own)

SideConfig is where you bind defaultPrompt, defaultModel, and the session lifecycle bindings:

• sessionStop — name of the tool whose call ends the session with a result
• sessionFail — name of the tool whose call ends the session with a failure
• sessionStatus — optional, a tool used to update the session status mid-run

These are the bindings the Session boundary discipline section refers to. Every dual_ai agent must set them.

Packaging fields (packageName, version, author, license) exist for the agent packing system; ignore them unless you're publishing.

SubagentToolConfig cheat sheet

This is where parent/child architecture is actually configured — it lives on the parent prompt's tools array. Knowing every field exists is essential; full signatures live in the spec types (node_modules/@standardagents/spec/dist/, or packages/spec/src/ on GitHub).

SubagentToolConfig — entry on a parent prompt's `tools` array
  name                       string                      dual_ai agent to invoke (must have exposeAsTool: true)
  blocking                   boolean (default true)      parent waits for result, vs. fire-and-forget
  immediate                  bool | object               spawn child when prompt activates, before any LLM step
                                                         object form: { nameEnv, descriptionEnv, scopedEnv }
                                                         scopedEnv values are copied into the child but NOT
                                                         exposed to the bootstrap model unless named in
                                                         nameEnv/descriptionEnv
  resumable                  false | object              false = tool-call style (default)
                                                         object = long-lived addressable child; runtime
                                                         exposes built-in subagent_create / subagent_message
    receives_messages          'side_a' | 'side_b'       which side hears parent messages
                                                         side_a = queued as 'user'
                                                         side_b = queued as 'assistant'
    maxInstances               number                    cap on concurrent instances; when reached the tool
                                                         is hidden and new messages route to existing instances
    parentCommunication        'implicit' | 'explicit'   implicit (default) auto-queues completion to parent
                                                         explicit requires state.notifyParent() in code
  initUserMessageProperty    schema field name           tool arg used as child's first user message
  initAttachmentsProperty    schema field name           tool arg holding /attachments/* paths to copy to child
  initAgentNameProperty      schema field name           tool arg used to tag the child thread (UI label)
  optional                   string (env name)           subagent disabled unless this env resolves truthy

A second tool-config form exists for plain callables ({ name, env, options }), and a third for subprompts ({ name, includeTextResponse, includeToolCalls, includeErrors, initUserMessageProperty, initAttachmentsProperty }). Both are documented in full at agents/prompts/AGENTS.md.

Inter-agent communication rules

These are correct as written in the spec — internalize them.

1. Parents always create children.
   • Explicitly via the built-in subagent_create tool, which requires a non-empty name for the child instance.
   • Implicitly via immediate: { ... } — the child spawns the moment the parent prompt activates, before any LLM step.
2. Children only communicate back to their parents. Two flavors:
   • Implicit: the child auto-queues a message to the parent when the session ends (via sessionStop, sessionFail, or maxSessionTurns). Default for all subagents.
   • Explicit: only when state.notifyParent() is called. Set with resumable.parentCommunication: 'explicit'. Use for high-traffic resumables (e.g., a Slack monitor) where you want control over when the parent is interrupted.
3. Resumable subagents can receive messages from their parents. resumable.receives_messages chooses which side hears them.
4. When a child sends back to the parent, it MUST indicate whether it needs a response — in plain English, e.g. "After researching, you must respond so I can continue with the email." A research subagent providing FYI info doesn't need a response; a gmail subagent waiting on guidance does.
5. File attachments use path strings. /attachments/{filename}.{ext}. Use this convention everywhere — the runtime copies attachments across thread filesystems automatically when listed in initAttachmentsProperty.
6. Resumable agents communicate via "silent" user messages that carry the source instance UUID. The receiving agent decides whether to respond or just absorb the information.
7. Messages to busy agents are queued and delivered when the agent is free.
8. Prompt language must distinguish subagent_create from subagent_message. When writing prompts that include resumable subagents, explicitly tell the model: "To research a new topic, use subagent_create. To follow up on a topic an existing instance is already researching, use subagent_message." Without this guidance, models will pick wrong.

---

Hooks

Hooks extend agent behavior without modifying core logic. Defined via defineHook, referenced by id from agent or prompt definitions. Common uses: truncating history, injecting synthetic tool calls (e.g., real-time clock awareness), logging, and adapting tool results.

Hook	Execution Point	Purpose
after_thread_created	After thread creation, before execution	Initialize thread state
after_subagent_created	On parent after child thread creation	Track or initialize child relationships
after_system_message	After system message render	Transform dynamic system instructions
filter_messages	Before LLM context assembly	Filter/transform message history
prefilter_llm_history	After context assembly	Final adjustments before LLM request
before_create_message	Before message insert	Transform message before storage
after_create_message	After message insert	Side effects after storage
before_update_message	Before message update	Transform update data
after_update_message	After message update	Side effects after update
before_store_tool_result	Before tool result storage	Transform tool results
after_tool_call_success	After successful tool call	Post-process success results
after_tool_call_failure	After failed tool call	Handle/recover from errors

Caution: Hooks that filter or truncate messages must keep matching tool calls and tool results together. Separating them produces hard-to-debug failures in many models.

Variables and environment

Variables let tools, prompts, and agents declare dynamic values they need. Declare them on prompts, tools, and agents with variables:

variables: [
  {
    name: 'LOCATION',
    type: 'text',
    required: true,
    description: 'City or ZIP code to use for weather lookups.',
  },
  {
    name: 'WEATHER_API_KEY',
    type: 'secret',
    required: true,
    description: 'Weather API credential used only inside tools.',
  },
];

Two value types are supported:

• text — simple string. Safe to render in prompts.
• secret — encrypted; MUST only be used inside tools. Never reference a secret in prompt text and never return it to the model. A GMAIL_API_KEY is secret; a LOCATION is text.

When a thread is created, all required variables in the agent graph must be provided. The instance of a variable on a thread is called an "environment variable" or env. Resolution precedence (low → high): prompt → tool → agent → thread.

Scoped variables (scoped: true) do not inherit from parent thread env — they reset for the declaring agent's subtree. Use this when a subagent must run with different config from its parent (e.g., a per-instance Slack channel ID).

Thread env values are editable from the thread metadata UI. ThreadState.setEnv(name, "") intentionally creates a blank thread env entry that still appears in that UI. Prefer the explicit form when writing new values:

await state.setEnv('LOCATION', 'Charlottesville, VA', { type: 'text' });
await state.setEnv('WEATHER_API_KEY', token, { type: 'secret' });

In code, read values through await state.env('NAME'), check display policy with await state.envType('NAME'), and write thread-scoped values with await state.setEnv('NAME', value, { type: 'text' | 'secret' }). Use text only for values that are safe in prompts, tool output, logs, and errors. Use secret for tokens, API keys, credentials, and anything that should be redacted.

Undeclared thread-only env keys are treated as write-only secrets when scanned for the UI, so the key is visible and editable but arbitrary stored values are not echoed back.

---

Implementation checking

When you edit agents/, prompts/, models/, tools/, hooks/, or worker/, validate before claiming the change is done.

Validation order:

1. Read package.json and prefer project scripts if they exist.
2. Refresh Cloudflare types:
   • use pnpm cf-typegen if that script exists
   • otherwise run npx wrangler types
3. Regenerate AgentBuilder types by running the project's build command:
   • usually pnpm build, npm run build, pnpm run dev, or equivalent
4. Run the project's type checker:
   • prefer pnpm type-check / npm run type-check if present
   • otherwise use the installed checker directly: pnpm exec vue-tsc --build, pnpm exec tsc -b, or pnpm exec tsc --noEmit
5. If any validation step cannot run, state exactly what is missing.