Reference: Agent profiles

The Generic Agent Runner exposes a set of registered profiles. Each profile is a declarative record pairing a system prompt with a tool kit and a target deliverable. Callers select a profile by id via agents.start; the runner loads the profile, renders the prompt against the task context, and drives the Anthropic tool-use loop.

This page is the canonical catalog of every profile currently registered on the platform. The profiles registered on the platform are the source of truth; if this doc disagrees, the platform wins and this doc is a bug.

At a glance

All four profiles are version 1.0.

content_writer

End-to-end content creation. Researches the topic (only when the caller has not pre-supplied research), outlines via content tools, writes a full draft, evaluates quality, and finalizes an artifact of type content_piece (or a more specific subtype such as linkedin_post or blog_post when input_params strongly implies one).

Config

• id: content_writer
• version: 1.0
• name: Content Writer
• description: End-to-end content creation agent. Researches, outlines, writes, and evaluates content. Produces a platform_artifact of type content_piece.
• model: claude-sonnet-4-6
• temperature: 0.3
• maxTurns: 25
• producesArtifactType: content_piece

Tool ids (glob-expanded at runtime)

content.*
data.*
context.*
context_substrate.*
context_entries.*
artifacts.create
artifacts.update
knowledge_base.search

Input params

Use case examples

• Draft a blog post on a topic grounded in internal research.
• Write a LinkedIn post in a specific voice with cited sources.
• Produce a newsletter issue from a supplied set of reference documents.

Sample agents.start

{
  "profile_id": "content_writer",
  "task": "Write a 900-word blog post on how mid-market SaaS teams evaluate data quality tooling.",
  "input_params": {
    "channel": "blog",
    "content_type": "blog_post",
    "audience": "VP Data at mid-market SaaS",
    "voice_profile_id": "22222222-2222-2222-2222-222222222222"
  },
  "async": true
}

researcher

Deep research agent. Explores a topic against the business's internal substrate and knowledge base, synthesizes findings with citations, and finalizes one artifact of type research_report. Scoped narrower than content_writer: no content tools, no artifact updates. Read-heavy.

Config

• id: researcher
• version: 1.0
• name: Researcher
• description: Deep research agent. Explores topics against substrate + knowledge base, synthesizes findings with citations, and produces a research_report artifact.
• model: claude-sonnet-4-6
• temperature: 0.2
• maxTurns: 20
• producesArtifactType: research_report

Tool ids

data.*
context.*
context_substrate.query
knowledge_base.search
knowledge_base.chat
artifacts.create

Input params

Use case examples

• Produce a competitive briefing on a specific company or segment.
• Synthesize a cross-doc answer grounded in a customer-interview library.
• Run a deep dive on a product area to seed a later content run.

Sample agents.start

{
  "profile_id": "researcher",
  "task": "Research how mid-market SaaS teams currently evaluate data quality tools; produce a report with key findings and gaps.",
  "input_params": {
    "audience": "VP Data at mid-market SaaS",
    "depth": "thorough",
    "must_cover": [
      "dimensions they actually score on",
      "where existing tools fail in their workflow"
    ]
  },
  "async": true
}

copilot

General-purpose assistant. Gets the broadest tool surface (every registered tool except destructive and privileged ones: artifact deletion, archival, api keys, oauth clients, audit log). Does not commit to producing an artifact. The tool list is resolved at task-start time against the live registry, so new tools registered after boot are automatically available (except those explicitly excluded).

Config

• id: copilot
• version: 1.0
• name: Copilot
• description: General-purpose assistant. Broad tool surface minus destructive and privileged admin tools. May or may not produce an artifact depending on the task.
• model: claude-sonnet-4-6
• temperature: 0.4
• maxTurns: 15
• producesArtifactType: none (discretionary)

Tool ids (resolved at runtime)

Every registered tool id, minus:

artifacts.delete
artifacts.archive
api_keys.*
oauth_clients.*
audit_log.*

Input params

No fixed schema. Callers can pass any free-form parameters in input_params; the profile's initial-message template renders each key verbatim so the model can pick up on it.

Use case examples

• Answer a question grounded in substrate without producing an artifact.
• Make a small edit to an existing artifact (artifacts.update) without starting a full writing run.
• Investigate a one-off substrate question and append a context entry.
• Mixed tasks where the agent should decide whether a saved deliverable is warranted.

Sample agents.start

{
  "profile_id": "copilot",
  "task": "Summarize our top three customer complaints from the last 30 days of interactions.",
  "input_params": {},
  "async": false
}

orchestrator

Multi-step planner. Decomposes a complex user ask into a sequence of steps by reading a matching agent_blueprint recipe and carrying out its steps itself, calling the primitive tools (data.*, context.*, knowledge_base.*, external_search.*, artifacts.*) directly. Blueprints are read-and-walk recipes: there is no server-side runner and no hand-off tool. Use for multi-step workflows with cross-step dependencies. For a single quick delegation, prefer the copilot's direct agents.start pattern.

Config

• id: orchestrator
• version: 1.0
• name: Orchestrator
• description: Multi-step planner. Reads a matching agent_blueprint recipe and walks its steps itself with primitive tools. Does not spawn sub-agents directly.
• model: claude-sonnet-4-6
• temperature: 0.2 (deterministic planning)
• maxTurns: 50
• producesArtifactType: none directly. The orchestrator walks a recipe and writes whatever artifacts the recipe's steps produce.

Tool ids (resolved from scope bundle)

The orchestrator uses a positive scope bundle (ORCHESTRATOR_GRANTED_SCOPES) so new tools register automatically when the relevant scope is granted. Currently surfaces every tool in:

artifacts:read, context:read, context_entries:read, content:read,
data:read, team:read, observability:read, workflows:read,
config:read, knowledge_base:read,
artifacts:write

The artifacts:write scope gives the orchestrator artifacts.create / artifacts.update so it can persist the outputs of the recipe steps it walks. It deliberately does NOT hold workflows:delegate, so agents.start is structurally unreachable: the orchestrator does its own work rather than spawning sub-agents. It also has no *:delete, keys:*, oauth_clients:*, audit_log:*, platform:*, or billing:* scope.

Input params

The orchestrator reads the task field plus any structured input_params you supply, and treats both as decomposition inputs. There are no required keys; common keys include:

Use case examples

• "Plan and draft next week's content with research + competitive context" -> read the matching recipe, run research, then draft the top pieces.
• "Investigate competitor X across web + internal interactions, then draft a battlecard" -> research first, then write the battlecard.
• "Refresh our weekly briefing across all topics" -> research per topic, then write the briefing.

Sample agents.start

{
  "profile_id": "orchestrator",
  "task": "Plan and draft next week's content, grounded in competitor activity and our most recent customer interactions.",
  "input_params": {
    "deadline": "2026-04-30",
    "channels": ["linkedin", "blog"]
  },
  "async": true
}

The orchestrator reads the matching agent_blueprint recipe, walks its steps with its own primitive tools, and replies with a summary of what it produced plus any artifact ids it wrote along the way. See Agent blueprints for the recipe DSL and the starter set the orchestrator draws on.

See also

• Agents guide for session lifecycle, SSE, and resume semantics
• Agent blueprints for the recipe DSL the orchestrator reads and walks