Overview

Interactive Agents — build, configure, and operate AI agents that hold conversations, run typed automations, call tools, and ground answers in your knowledge base.

Context — This is the documentation root for Interactive Agents: the framework and runtime for deploying AI agents on the InteractiveAI platform. It is self-contained: everything you need to design, build, test, deploy, and operate an agent is in these pages. No source-code access is required or assumed.

What an Interactive Agent is

An Interactive Agent is a containerized service (the agent server) that runs one AI agent. The agent's entire identity and behaviour is declared in configuration — there is no per-agent code:

• A manifest (YAML) declares who the agent is, which model(s) it uses, which content it loads, and which systems it connects to.

• Policies are condition → action rules that shape behaviour on every turn (safety, compliance, tone, business rules).

• Routines are multi-step state machines that walk the agent through structured flows (look up an order, book a car, verify a document).

• Tools are functions the agent can call, served by MCP servers you run.

• A knowledge base (optional) grounds answers in your documents.

• A versioned system prompt and glossaries define persona and domain vocabulary.

Agents operate in two modes, often simultaneously:

Mode	Trigger	Result delivery
Conversational	A customer message (via the SDK or your integration)	Typed events streamed back (replies, tool calls, status)
Autonomous	POST /routines/{id}/trigger or a third-party webhook	A typed JSON result delivered to your callback URL

How this documentation is organized

Section	Read it when you want to…
Concepts	Understand how the system works — architecture, the turn lifecycle, every configuration concept
Guides	Do something — build your first agent, author routines, connect tools, deploy
Reference	Look up an exact field, endpoint, env var, or default
Operations	Run agents in production — troubleshooting, security, versioning

Pages under reference/ marked generated: true are produced directly from the runtime's source of truth on every release and are exact for the version they document.

Reading paths

"I want a working agent today" → Quickstart → Authoring routines → Integrating the SDK → Deploying

"How do my systems and the agent talk to each other?" → Integration overview — every traffic direction (SDK, event delivery, triggers, callbacks, webhooks, tools) on one page, with links

"I need to understand the model before I build" → Architecture → Conversation lifecycle → Policies → Routines

"I'm wiring a backend automation, not a chat UI" → Autonomous routines → Authoring autonomous routines → Events & callbacks reference

"I operate a deployed agent" → Observability → Troubleshooting → Security

For AI agents reading these docs

• llms.txt is a one-line-per-page index with stable links.

• llms-full.md is the entire documentation concatenated into a single file, regenerated on every release — fetch it once and you have the full corpus.

• Machine-readable JSON Schemas for the manifest, routines, policies, glossaries, conversation events, and autonomous callbacks are published per version; see Versioning for the download locations.

• Every code block in these docs is complete and copy-pasteable — nothing is elided.

The example used throughout

All guides share one worked example: DriveAway, a car-rental agent named Mercedes with six routines (car search, booking, manage booking, locations, loyalty lookup, member signup), seven policies (minimum driver age, licence requirement, stay-on-topic, pricing disclaimer, cross-border restrictions, one-way surcharge, incident handoff), and one MCP tool server backing the booking system. The Quickstart builds it from scratch.