Agent API

The AI agent reads conversations, retrieves KB context, calls an LLM, and posts a response (autopilot) or drafts one for review (copilot).

Endpoints

Status

bash
curl https://api.rogeriq.com/api/v1/projects/prj_xxx/agent/status \  -H "X-API-Key: riq_xxx"

bash
rogeriq agent status

ts
const status = await roger.agent.status();

json
{  "data": {    "enabled": true,    "mode": "copilot",    "model": "anthropic/claude-sonnet-4",    "domain": "support.acme.com",    "has_byom_key": false,    "usage_this_month": {      "calls": 1284,      "input_tokens": 845022,      "output_tokens": 184211,      "cost_usd": 12.40    }  }}

Update config

bash
curl -X PATCH https://api.rogeriq.com/api/v1/projects/prj_xxx/agent/config \  -H "X-API-Key: riq_xxx" \  -H "Content-Type: application/json" \  -d '{"agent_mode": "autopilot", "ai_model": "openai/gpt-4o"}'

bash
rogeriq agent config --mode autopilot --model openai/gpt-4o

ts
await roger.agent.updateConfig({  agent_mode: "autopilot",  ai_model: "openai/gpt-4o",});

Respond on a conversation

bash
curl -X POST https://api.rogeriq.com/api/v1/projects/prj_xxx/agent/respond \  -H "X-API-Key: riq_xxx" \  -H "Content-Type: application/json" \  -d '{"conversation_id": "con_xxx"}'

Runs the full agent pipeline:

1. Loads conversation history + contact metadata.
2. Hybrid-searches the KB for relevant context.
3. Builds the system prompt + calls the LLM.
4. Posts the response according to the project's agent_mode.

json
{  "data": {    "content": "Hi Jane! ...",    "content_html": "<p>Hi Jane! ...</p>",    "confidence": 0.82,    "mode": "copilot",    "effective_mode": "copilot",    "message_created": true,    "message_id": "msg_xxx",    "sources": [{ "id": "kba_xxx", "title": "How refunds work" }],    "usage": { "input_tokens": 4500, "output_tokens": 230, "cost_usd": 0.04 }  }}

mode_override in the body forces a specific mode for this call only.

Classify

bash
curl -X POST https://api.rogeriq.com/api/v1/projects/prj_xxx/agent/classify \  -H "X-API-Key: riq_xxx" \  -H "Content-Type: application/json" \  -d '{"conversation_id": "con_xxx"}'

Returns intent, sentiment, suggested priority, and tags. Useful for triage automation:

ts
for await (const conv of roger.conversations.listAll({ status: "open" })) {  const c = await roger.agent.classify(conv.id);  if (c.priority === "urgent") {    await roger.conversations.update(conv.id, { priority: "urgent" });  }}

Suggest (without posting)

bash
curl -X POST https://api.rogeriq.com/api/v1/projects/prj_xxx/agent/suggest \  -H "X-API-Key: riq_xxx" \  -H "Content-Type: application/json" \  -d '{"conversation_id": "con_xxx"}'

Same retrieval + generation pipeline as respond, but never posts. Returns the suggested text + sources for human review.

Audit

Every agent.config.updated and agent.responded event is captured in the audit log with the acting API key id.