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Intent Recognition and Auto‑Routing in Multi-Agent Systems

Modern conversational AI systems often split functionality into multiple tools or sub-agents, each specialized for a task (e.g. search, booking, math, etc.). When a user sends a query, the system must interpret intent and dispatch it to the right tool/agent. There are two broad approaches: letting a general-purpose LLM handle intent detection itself, or using a dedicated router component. In practice, many practitioners use a hybrid: an initial “router” classifies the intent and then a specialized agent or tool handles the task. Below we survey best practices and examples of each approach, referencing frameworks like LangChain and Semantic Router.

LLM-Based Intent Recognition (General-Agent Approach)

A common approach is to have the LLM itself decide which tool or chain to invoke. For example, one can prompt the model to output a JSON field indicating the desired “tool” or “function” (using OpenAI’s function-calling or ChatGPT Pl

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Background Task Queue in an AI Application

Chat-based AI apps often need to perform long-running operations (e.g. document generation, data analysis) that can’t block the user interaction. To keep the system responsive, these tasks must run in the background, outside the normal request–response cycle. A common pattern is to introduce an asynchronous task queue: the user’s request is immediately enqueued (e.g. returning “task accepted”) ([Background Tasks – FastAPI])([Using FastAPI with SocketIO to Display Real-Time Progress of Celery Tasks | by Fadi Shaar | Medium]), and a separate process (or cluster of workers) executes the job. This way, FastAPI can instantly handle further requests while heavy work proceeds independently, keeping the API responsive for other clients ([Using FastAPI with SocketIO to Display Real-Time Progress of Celery Tasks | by Fadi Shaar | Medium])([Background Tasks – FastAPI]).

For example, a contract-generation chatbot can immediately confirm receipt (“Document generation in progr

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Best Practices for Agent-Friendly API Design

When designing APIs intended for intelligent agents (conversational LLMs, multi-agent orchestration, etc.), it’s helpful to treat them as “machine interfaces”—clear and unambiguous not only to developers, but to algorithms as well. A good starting point is to produce a full OpenAPI specification for your service (for example using FastAPI, which automatically generates Swagger/OpenAPI docs). The OpenAPI standard lets agents read the entire API definition—what resources and parameters are available, how to authenticate, what inputs to send, and what responses to expect ([Building an AI agent with OpenAPI: LangChain vs. Haystack]). Crafting complete, AI-ready documentation is critical ([Is Your API AI-ready? Our Guidelines and Best Practices]).

• Rich descriptions and metadata. Every endpoint and parameter should have an exhaustive description—not just a repeat of its name, but an explanation of “what this endpoint does,” “what data it expects,” and

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Agentive Mechanisms: Handoff vs. Agent-as-Tool

In multi-agent systems, agents can collaborate in two primary ways: handoff (transferring control) or agent-as-tool (agent as a tool). In the handoff pattern, one agent completes its part of the work and passes the entire context to the next “specialist” agent instead of continuing to process it itself (Handoffs — AutoGen) (Multi-agent systems – Agent Development Kit). Conversely, the agent-as-tool pattern has the main agent invoke a secondary agent like a function or API call—then integrates its response into the ongoing conversation ([Multi-agent systems – Agent Development Kit](https://google.github.io/adk-docs/agents/multi-agents/#:~:text=,invocation%20like%20any%20other%20to

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Managing Context and Quality in Long Conversations

A multi-agent conversational system requires awareness of the memory limitations of language models. Large models (GPT-4, Claude, Mistral, Gemini) do offer very wide context windows, but they still operate on a “sliding window” basis. This is illustrated in Fig. 1 – when the context window fills up, new tokens “push” older ones out of the model’s memory window, causing earlier information to be lost. In practice, this means that over time, during a long conversation the model can forget earlier turns, start repeating itself, or make coherence errors. This phenomenon is sometimes called Context Degradation Syndrome. After just a few dozen to a few hundred exchanges, the model can “lose the thread” and generate increasingly imprecise answers ([Context Degradation Syndrome: When Large Language Models Lose the Plot](https://jameshoward.us/2024/11/26/context-degradation-syndrome-when-large-language-models-lose-the-plot#:~:text=As%20conversations%20len