Creating Agents

Agents are created with the agent:create operation. The key decision is what to put in the config — this determines the agent's personality, tools, capabilities, and LLM backend.

Minimal Agent

The simplest possible agent has just an ID:

{
  "operation": "agent:create",
  "input": {
    "agentId": "my-agent"
  }
}

This creates an agent with default settings: llmagent:chat transition, langchain:openai backend, gpt-4o model, and the platform's default tool set. It will respond conversationally to any task.

Configuration

The config field controls agent behaviour. All fields are optional — defaults are sensible for general-purpose agents.

{
  "operation": "agent:create",
  "input": {
    "agentId": "Bob",
    "config": {
      "operation": "v/ops/goaltree/chat",
      "systemPrompt": "You are Bob, an AP Data Enricher...",
      "model": "gpt-4o-mini",
      "llmOperation": "v/ops/langchain/openai",
      "tools": [
        "v/ops/covia/read",
        "v/ops/covia/write",
        "v/ops/covia/list"
      ],
      "defaultTools": false,
      "caps": [
        { "with": "w/vendor-records/", "can": "crud/read" },
        { "with": "w/enrichments/", "can": "crud" }
      ],
      "context": [
        { "ref": "w/docs/data-guide", "label": "AP Data Guide" }
      ],
      "outputs": {
        "complete": {
          "schema": {
            "type": "object",
            "properties": {
              "enrichedData": { "type": "object" },
              "confidence": { "type": "number" }
            },
            "required": ["enrichedData", "confidence"]
          }
        }
      }
    }
  }
}

Config Reference

Field	Type	Default	Description
operation	string	v/ops/llmagent/chat	Level 2 transition function. Use v/ops/goaltree/chat for goal tree.
systemPrompt	string	Generic assistant	The system message defining the agent's role and instructions.
model	string	gpt-4o	LLM model name passed to the backend.
llmOperation	string	v/ops/langchain/openai	Level 3 LLM operation. See LLM Backends.
tools	array	—	Operations the agent can call. Strings or {operation, name, description} maps.
defaultTools	boolean	true	Include the platform's default tools (workspace CRUD, agent management, assets, schemas). Set false for explicit control.
caps	array	—	Capability restrictions. Each entry: {with: "path/", can: "crud/read"}.
context	array	—	Lattice paths to load into context every turn.
outputs	object	—	Typed output schemas for complete and fail (Goal Tree only).
responseFormat	object	—	JSON Schema for structured responses.

Tools

The tools array lists operations the agent can invoke. Each entry is either:

• A string — an operation path like "v/ops/covia/read"
• A map — { "operation": "v/ops/covia/read", "name": "read_data", "description": "Read workspace data" } for a custom name/description

When defaultTools is true (the default), the platform adds standard operations covering workspace CRUD, agent management, asset operations, and schema validation. Set defaultTools: false for full control over the tool palette.

Capabilities

Capabilities restrict what paths and operations an agent can access. They follow the UCAN model:

"caps": [
  { "with": "w/vendor-records/", "can": "crud/read" },
  { "with": "w/enrichments/", "can": "crud" }
]

The with field is a path prefix; can specifies the permission level (crud/read, crud/write, crud, etc.). Capabilities are disclosed to the agent in its system prompt so it knows what it can and cannot do. If a tool call is denied, the error message lists the agent's available capabilities.

Context

The context array injects external data into the agent's context every turn:

"context": [
  { "ref": "w/docs/data-guide", "label": "AP Data Guide" },
  { "ref": "w/vendor-records/acme", "label": "ACME Record" },
  { "op": "v/ops/covia/list", "input": { "path": "w/vendors" }, "label": "Known Vendors" }
]

Each entry can be:

• ref — a lattice path, resolved and rendered fresh each turn
• op — an operation to invoke, whose output is injected as context

Context entries are budget-aware — large values are automatically summarised to fit within the context window.

Typed Outputs (Goal Tree)

When using the Goal Tree adapter, you can enforce structured output via outputs:

"outputs": {
  "complete": {
    "schema": {
      "type": "object",
      "properties": {
        "result": { "type": "string" },
        "confidence": { "type": "number" }
      },
      "required": ["result", "confidence"],
      "additionalProperties": false
    }
  },
  "fail": {
    "schema": {
      "type": "object",
      "properties": {
        "reason": { "type": "string" },
        "details": { "type": "string" }
      },
      "required": ["reason"],
      "additionalProperties": false
    }
  }
}

This wraps the complete and fail harness tools with strict JSON schema enforcement via the LLM's structured output mode.

Templates

Instead of inline config, you can reference a template:

{
  "operation": "agent:create",
  "input": {
    "agentId": "reader-1",
    "config": "template:reader"
  }
}

Standard Templates

Every venue provides these built-in templates:

Template	Tools	Purpose
template:minimal	None	Pure reasoning, no side effects
template:reader	read, list, slice	Read-only data analysis
template:worker	CRUD operations	General data processing
template:manager	Agent ops + read/list	Agent coordination
template:analyst	CRUD + schema ops	Data analysis with validation
template:full	All defaults	Development and exploration

Custom Templates

Store a config map in the workspace and reference it:

// Store a template
{
  "operation": "covia:write",
  "input": {
    "path": "w/templates/invoice-scanner",
    "value": {
      "operation": "v/ops/goaltree/chat",
      "systemPrompt": "You are an invoice scanner...",
      "model": "gpt-4o-mini",
      "defaultTools": false,
      "outputs": { "complete": { "schema": { ... } } }
    }
  }
}

// Create agent from template
{
  "operation": "agent:create",
  "input": {
    "agentId": "scanner-1",
    "config": "w/templates/invoice-scanner"
  }
}

Templates can also be stored as assets (immutable, versioned) or referenced by DID URL for cross-venue use.

Initial State

An agent can be created with pre-populated state by including a state field at the top level:

{
  "operation": "agent:create",
  "input": {
    "agentId": "Alice",
    "config": { "operation": "v/ops/goaltree/chat" },
    "state": {
      "config": {
        "model": "gpt-4o-mini",
        "systemPrompt": "You are Alice...",
        "tools": ["v/ops/covia/read"]
      }
    }
  }
}

The state is opaque to the framework — its structure depends on the Level 2 adapter. For both llmagent:chat and goaltree:chat, the state includes a config sub-map with LLM settings.

Idempotent Creation

By default, agent:create is idempotent. If an agent with the given ID already exists:

• The call succeeds (no error)
• The existing agent is unchanged
• The response indicates created: false

To replace an existing agent, use overwrite: true:

{
  "operation": "agent:create",
  "input": {
    "agentId": "Alice",
    "overwrite": true,
    "config": { "systemPrompt": "Updated instructions..." }
  }
}

This updates the config in place for SLEEPING or SUSPENDED agents. For TERMINATED agents, it revives the agent with fresh state.

AP Demo Example

The AP Invoice Audit Trail demo creates a team of specialised agents:

// Alice: Invoice Scanner (minimal tools, structured output)
{
  "agentId": "Alice",
  "config": { "operation": "v/ops/goaltree/chat" },
  "state": { "config": {
    "model": "gpt-4o-mini",
    "defaultTools": false,
    "systemPrompt": "You are Alice, an AP Invoice Scanner...",
    "outputs": { "complete": { "schema": { ... } } }
  }}
}

// Bob: Data Enricher (workspace access, vendor lookup)
{
  "agentId": "Bob",
  "config": { "operation": "v/ops/goaltree/chat" },
  "state": { "config": {
    "model": "gpt-4.1-mini",
    "tools": ["v/ops/covia/read", "v/ops/covia/write", "v/ops/covia/list"],
    "caps": [
      { "with": "w/vendor-records/", "can": "crud/read" },
      { "with": "w/enrichments/", "can": "crud" }
    ],
    "context": [
      { "ref": "w/docs/data-guide", "label": "AP Data Guide" }
    ],
    "outputs": { "complete": { "schema": { ... } } }
  }}
}

// Carol: Payment Approver (read all, write decisions)
{
  "agentId": "Carol",
  "config": { "operation": "v/ops/goaltree/chat" },
  "state": { "config": {
    "model": "gpt-4o",
    "tools": ["v/ops/covia/read", "v/ops/covia/write", "v/ops/covia/list"],
    "caps": [
      { "with": "w/", "can": "crud/read" },
      { "with": "w/decisions/", "can": "crud" }
    ],
    "outputs": { "complete": { "schema": { ... } } }
  }}
}

Each agent has the minimum tools and capabilities needed for its role — following the principle of least privilege.

Related

• Agent Operations — full lifecycle reference (trigger, request, fork, etc.)
• Tools and Context — tool resolution, context loading, budgets
• LLM Backends — configuring OpenAI, Anthropic, Ollama