Conversational AI Architecture

Conversational AI architecture is best understood as a set of loops rather than a single model call. Each loop receives user input, updates context, decides what to do, produces output, and records enough data to improve the next interaction.

Pattern 1: Text Chat With Tools

The simplest production pattern looks like this:

User -> App -> LLM -> Tool calls -> Response

This works well for support assistants, internal knowledge bots, sales qualification, and workflow helpers.

The important parts are:

• Clear system instructions.
• Narrow tool schemas.
• Input validation outside the model.
• Conversation state.
• Error handling.
• Human handoff.

Pattern 2: RAG Assistant

Retrieval-augmented generation adds a knowledge layer:

User -> Query rewrite -> Retrieval -> Rerank -> LLM -> Answer

RAG fails when the right content is not retrieved, when the chunk lacks context, or when the model ignores the evidence. Evaluate retrieval separately from answer generation.

Pattern 3: Pipeline Voice Agent

A classic voice agent is a streaming pipeline:

Microphone -> VAD/turn detection -> STT -> LLM/tools -> TTS -> Speaker

This pattern is debuggable and provider-flexible. It also accumulates latency across every component. You need to measure end-of-turn delay, STT finalization, LLM time to first token, TTS time to first byte, and barge-in behavior.

Pattern 4: Realtime Speech-To-Speech Model

Realtime model APIs collapse parts of the pipeline:

Microphone -> realtime model session -> Speaker
                       |
                     tools

This can feel more natural, especially for speech and multimodal context. The tradeoff is control: debugging, tool policy, and provider portability can become harder.

Pattern 5: Phone Agent

Phone agents add telecom infrastructure:

PSTN -> SIP/media stream -> agent session -> tools/CRM -> voice response

Phone agents need caller identity, call routing, DTMF, transfer, recording consent, regional compliance, and a clear human escalation path.

Pattern 6: Multimodal Agent

Multimodal agents combine voice, text, image, video, screen, documents, and UI state.

The most practical first version is often hybrid: continuous audio, but visual context only when the user asks or when the UI emits an event. This reduces privacy and cost risk.

The Production Layer

Every architecture needs an operations layer:

• Traces and transcripts.
• Tool call logs.
• Latency spans.
• Cost tracking.
• Prompt and tool versions.
• Evals and scenario tests.
• Human review.
• Privacy and retention rules.

Architecture is not only about data flow. It is about knowing what can fail, what can be measured, and who takes over when automation is not enough.