Modern AI systems are quickly moving from isolated chatbots to full-fledged agent platforms that can reason, search, route tasks, and operate across multiple model providers. In this blog, we’ll walk through the architecture and design of a Claude Agent SDK that integrates multiple LLM providers, including Mistral AI, along with enterprise search via Glean, and orchestration inspired by Anthropic-style agent workflows.

The goal is simple:

Build a single, production-ready SDK that can intelligently route tasks across models, index enterprise knowledge, and scale via Docker in minutes.

Why Build an Agent SDK Instead of a Single LLM App?

Most AI applications today fail at scale for three reasons:

They depend on a single model (fragility + cost inefficiency)
They lack retrieval infrastructure (no real “memory”)
They are hard to deploy consistently across environments

An Agent SDK solves this by design:

Multi-model routing (best model per task)
Built-in retrieval (enterprise + vector search)
Observability (cost, latency, tracing)
Production-first deployment (Docker, APIs, monitoring)

Core Architecture Overview

The system is built around four layers:

1. LLM Gateway (Multi-Provider Routing)

Instead of hardcoding a single model, we use a routing layer:

Anthropic models (Claude Opus / Sonnet)
Mistral AI models (Mistral Large / Medium / Small)
OpenAI-compatible models (GPT-4, GPT-4o-mini)
Local models (Ollama for privacy-first workloads)

This is powered by a unified abstraction (similar to LiteLLM-style routing), which selects models based on:

Task complexity
Cost constraints
Latency requirements

2. Agent Execution Engine

At the heart of the SDK is the agent loop:

Receives a task
Classifies intent
Routes to appropriate LLM
Optionally queries knowledge index
Returns structured output

This transforms LLM usage from:

“prompt → response”

to:

“task → reasoning → tool use → retrieval → response”

3. Enterprise Search Layer (Glean Indexer)

A major limitation of LLMs is lack of real-time organizational context.

We solve this using Glean:

Index internal documents
Enable semantic search
Feed retrieved context into LLM prompts
Support RAG (Retrieval-Augmented Generation)

This allows agents to answer:

Internal engineering questions
Product documentation queries
Policy and compliance questions

4. Production Infrastructure (Docker + Observability)

The system ships with a full production stack:

FastAPI backend (agent API)
LiteLLM proxy (model routing layer)
PostgreSQL (state + logs)
Redis (caching)
Prometheus + Grafana (metrics)
Langfuse (LLM tracing)

Everything is orchestrated via Docker Compose.

Key Features of the SDK

1. Smart Model Routing

The SDK automatically selects the best model:

Mistral Small → simple tasks (cheap)
Claude Sonnet → reasoning tasks
Claude Opus → complex agent workflows
GPT-4o → structured outputs

2. Cost Optimization Engine

Every request tracks:

Token usage
Cost per provider
Latency benchmarks

This ensures you can scale without losing control of spend.

3. Streaming Agent Responses

Instead of waiting for full responses:

Tokens stream in real time
Enables chat-like UX for APIs
Supports long-running reasoning tasks

4. Glean-Powered Retrieval (RAG)

The agent can:

Search enterprise knowledge
Inject context dynamically
Reduce hallucinations significantly

5. Full Observability

With Langfuse + Prometheus:

Every prompt is traceable
Latency per model is visible
Cost breakdown per request is tracked

Deployment in One Command

One of the strongest aspects of this SDK is simplicity:

./quickstart.sh

This automatically:

Builds containers
Starts all services
Configures LLM routing
Initializes monitoring dashboards

Within minutes, you get a fully working AI agent platform.

Example API Usage

curl -X POST http://localhost:8000/v1/agent/execute \
  -H "Content-Type: application/json" \
  -d '{
    "task": "chat",
    "prompt": "Explain distributed systems simply"
  }'

Behind the scenes, the system:

Classifies the request
Selects the best model (likely Claude or Mistral)
Optionally retrieves context from Glean
Streams response back

Why This Architecture Works

This design succeeds because it treats LLMs as interchangeable reasoning engines, not fixed APIs.

Key advantages:

No vendor lock-in
Cost-aware routing
Enterprise-ready retrieval
Horizontal scalability via containers

Real-World Use Cases

This SDK can power:

🧠 Enterprise AI Assistants

Internal Slack bots, HR assistants, engineering copilots

🔍 Knowledge Search Systems

Semantic search across documentation + wikis + tickets

⚙️ AI Automation Agents

Task execution pipelines (tickets, emails, workflows)

📊 Analytics Agents

Natural language querying over structured data

Final Thoughts

The future of AI systems is not “one model to rule them all,” but intelligent orchestration across many specialized models and tools.

By combining:

Multi-provider LLM routing (Mistral AI + Anthropic)
Enterprise search (Glean)
Production-grade infrastructure

we move from simple chatbots to true agentic systems.

If you’re building anything serious with LLMs today, the shift is clear:

Stop building prompts. Start building systems.

Building a Production-Ready Claude Agent SDK with Mistral LLM and Glean Indexer