AgenticFlow Agent

A single AI agent with a system prompt, model, optional MCP tool attachments, and an optional code-execution sandbox. Use this when one chat surface + one set of rules is enough.

When NOT to use this skill

If the user needs multiple agents that hand off to each other (research → write, triage → specialist, a pre-built team template), use agenticflow-workforce instead. Don't over-engineer — a support bot with "if billing/refunds/privacy, escalate to email" is one agent, not three.

Orient first

af bootstrap --json

From the response, extract:

• auth.project_id — required on agent create (server does not auto-inject for agents, unlike workforces)
• auth.workspace_id
• _links.workspace — surface this URL to the user right away: "Your AgenticFlow workspace is at <_links.workspace> — open it anytime to see what I'm building." Anchors a human-first mental model before any mutation
• models[] — use as source of truth for model ids (don't hardcode — they change between CLI releases)
• agents[] — so you don't duplicate existing work

If data_fresh: false in the response, the backend is degraded — don't mutate. Run af doctor --json --strict and fix auth/network first.

Discovery & health

af changelog --json           # What's new in the CLI since your last install
af context --json              # AI agent orientation, env vars, invocation guidance
af bootstrap --strict --json   # Health check — exits non-zero if backend degraded

af bootstrap returns an invocation block telling you the correct CLI binary to use. af bootstrap --strict exits non-zero when the backend is unhealthy, so CI/automation can abort before mutating against a degraded workspace.

Inspect payload shape before writing

af schema agent --json
af schema agent --field mcp_clients --json          # Nested attach shape
af schema agent --field suggested_messages --json   # {title, label, action} — NOT strings
af schema agent --field response_format --json      # Structured output config
af schema agent --field update --json               # Update + null-rejected fields list

The --field drilldown returns the documented shape for a single field. Use it instead of guessing.

New fields (v1.10.x)

Field	Type	What it does
welcome_message	string	Greeting shown on new thread
agent_type	standard | autonomous	Default: standard
recursion_limit	number (10-500)	Default: 100 (was 25)
model_user_config	{ temperature?, max_tokens?, max_input_tokens?, reasoning_effort? }	Fine-tune the model
code_execution_tool_config	{ enable: bool, enable_file_operations?: bool }	Python/JS sandbox
file_system_tool_config	object | null	Enable file system tool
attachment_config	object | null	File attachment config
response_format	object | null	Structured output schema (JSON mode) for the final response
knowledge	object | null	Knowledge base / RAG configuration
skills_config	object | null	Skill pack configuration
task_management_config	object | null	Task queue / scheduling
sub_agents	array	Sub-agent configurations for agent teams
plugins	array	Plugin configurations (e.g. web_search, web_retrieval)
suggested_messages	[ { title, label, action } ]	Pre-populated prompts. NOT an array of strings — server rejects strings

Null-rejected fields on update

These fields must be OMITTED (not sent as null) on af agent update — the server rejects null:

suggest_replies_model, suggest_replies_model_user_config, suggest_replies_prompt_template,
knowledge, task_management_config, recursion_limit,
file_system_tool_config, attachment_config, response_format, skills_config

The CLI auto-strips them even without --patch. Stripped fields are logged to stderr.

af schema agent --field update --json   # See the full list

Create (always preview first)

af agent create --body @agent.json --dry-run --json
af agent create --body @agent.json --json

Minimum valid payload:

{
  "name": "My Support Assistant",
  "tools": [],
  "project_id": "<from bootstrap auth.project_id>",
  "model": "agenticflow/gpt-4o-mini",
  "system_prompt": "You are ..."
}

Default model change: Since CLI v1.8.1, the upstream default is agenticflow/gpt-4o-mini (was gemini-2.0-flash). GPT-4o-mini follows system prompts reliably and calls tools without refusing. Use your preferred model via --model <id>.

recursion_limit defaults to 100 (was 25). If an agent returns completed_empty, check if recursion_limit was lower.

Available models live in af bootstrap --json > models[] — always read from there rather than hardcoding a list in your logic (models ship between CLI releases). The CLI validates your model string at create time: typos fail fast with an actionable hint listing the known set. If you pass a vendor/model-name-shaped string not in the known list, it warns-but-proceeds so brand-new models work before the CLI is updated.

Run (smoke test)

af agent run --agent-id <id> --message "Test prompt" --json
# Returns {response, thread_id, status}.

af agent run --agent-id <id> --thread-id <tid> --message "continue" --json
# Pass the same thread_id to keep conversation context; omit it to start fresh.

af agent run --agent-id <id> --message "Test" --wait --timeout 60 --json
# Polls until terminal status. Exits code 2 if final status is failed/cancelled/error.

af agent stream --agent-id <id> --message "Test" --json
# SSE token-level streaming.

completed_empty (v1.8.2)

The backend sometimes returns {status: "completed", response: ""} when the agent exhausts its recursion_limit in a tool loop. The CLI now reclassifies this as:

{ "status": "completed_empty", "warning": "..." }

Exit code 2 — bash && chains halt automatically.

Remediation:

1. Inspect thread messages: af agent-threads messages --thread-id <tid> --json
2. Raise recursion_limit: af agent update --agent-id <id> --patch --body '{"recursion_limit":100}' --json
3. Refine prompt to reduce loop depth

Do NOT treat completed_empty as success — the response is empty.

---

Get agent (aliases + fields)

af agent get --agent-id <id> --json              # Canonical
af agent get --id <id> --json                     # Alias (v1.8.1+)
af agent get --id <id> --fields id,name,model --json   # Response projection

Iterate with --patch (the cornerstone pattern)

Never round-trip the full agent body to change one field:

# WRONG — full-body replace loses attached MCPs / tools / code_exec config if omitted
af agent update --agent-id <id> --body @updated.json

# RIGHT — partial update, everything else preserved
af agent update --agent-id <id> --patch --body '{"system_prompt":"new prompt"}' --json
af agent update --agent-id <id> --patch --body '{"model":"agenticflow/gpt-4o-mini"}' --json
af agent update --agent-id <id> --patch --body '{"mcp_clients":[{"mcp_client_id":"<id>","run_behavior":"auto_run","tools":{}}]}' --json

The CLI auto-strips null-rejected fields (knowledge, recursion_limit, task_management_config, suggest_replies_*, file_system_tool_config, attachment_config, response_format, skills_config). Stripped fields are logged to stderr so bots don't think they cleared a field they didn't.

Attach an MCP tool provider

See the agenticflow-mcp skill for the full inspect-before-attach flow. Short version:

af mcp-clients list --name-contains "google sheets" --fields id,name --json
af mcp-clients inspect --id <mcp_id> --json
# Only proceed if pattern != "pipedream" with write_capable_tools
af agent update --agent-id <agent_id> --patch --body '{"mcp_clients":[{...}]}' --json

Cleanup

af agent delete --agent-id <id> --json
# Returns {"schema":"agenticflow.delete.v1","deleted":true,"id":"...","resource":"agent"}

On errors

Every API error returns a consistent envelope with an actionable hint. Common 4xx and their hints:

• 404 → "Run the matching list command to see available IDs" (or double-check the ID)
• 422 → "Check details.payload for field-level errors" (pydantic returns the offending field)
• 401 → "Run af whoami / af login"

When hint is non-empty, follow it before retrying.