How Cloudflare AI Agent Lee Works | Generated by AI
Question: How does Cloudflare AI Agent “Lee” (Ask AI) work?
Answer:
The “Ask AI” feature you’re referring to is part of Cloudflare’s broader push into edge AI—often demonstrated through internal agents like “Lee” or similar assistants. While “Lee” itself isn’t a widely formalized public product name, it represents a pattern of how Cloudflare builds AI agents on its infrastructure.
Here’s how it works conceptually:
1. Core Idea: AI Runs at the Edge
Cloudflare operates a global edge network (data centers close to users). Instead of sending AI requests to centralized servers (like typical APIs), Cloudflare runs AI models near the user using:
- Cloudflare Workers
- Workers AI
This reduces:
- latency (faster responses)
- cost (less bandwidth + centralized compute)
- privacy risk (data stays closer to origin)
2. “Ask AI” = Retrieval + LLM + Edge Execution
An AI agent like “Lee” typically follows this pipeline:
Step 1: User Query
User asks something like:
“What does this API do?” or “Explain this doc”
Step 2: Retrieval (Context Injection)
Instead of blindly querying a model, the system:
- Searches relevant data (docs, code, logs, knowledge base)
- Uses vector search (embeddings)
Often powered by:
- Vectorize
This is called RAG (Retrieval-Augmented Generation).
Step 3: LLM Inference
The system sends:
- user question
- retrieved context
to a model via:
- Workers AI
Cloudflare supports open models (e.g., Llama, Mistral), not just proprietary ones.
Step 4: Agent Logic (Optional)
If it’s a true “agent” (like Lee), it can:
- call APIs
- chain reasoning steps
- execute actions (e.g., query logs, modify configs)
This is orchestrated inside:
- Cloudflare Workers
So the agent is basically:
code + AI model + tools
Step 5: Response at the Edge
The answer is generated and returned directly from the nearest Cloudflare node.
3. Why Cloudflare’s Approach Is Different
Traditional AI (e.g., OpenAI API)
- Centralized servers
- Higher latency
- Data leaves your environment
Cloudflare AI Agent
- Runs on edge network
- Lower latency (global distribution)
- Can integrate directly with your infra (logs, APIs, DBs)
4. Typical Use Cases
“Ask AI” / agents like Lee are used for:
- Documentation assistants (“ask your docs”)
- Dev tools (explain code, debug logs)
- Customer support bots
- Internal knowledge assistants
- Security analysis (logs, traffic patterns)
5. Mental Model
You can think of it like this:
Cloudflare AI Agent = (Serverless function) + (LLM) + (Search over your data) + (Tools/APIs) running globally at the edge
6. Simple Flow Diagram (Conceptual)
User Question
↓
Edge Worker (Cloudflare Workers)
↓
Retrieve context (Vectorize / KV / R2)
↓
Call LLM (Workers AI)
↓
Optional tool usage (APIs, logs)
↓
Response returned from nearest edge
7. Key Insight
The important shift is:
- Not “AI as a remote API”
- But AI as infrastructure embedded into your backend
Cloudflare is essentially turning AI into something like:
“Just another function call inside your serverless app”
If you want, I can break down a real minimal example (like building your own “Ask AI” agent using Cloudflare Workers + Workers AI) so you can see how little code it actually takes.