The architecture behind building an AI secretary that actually understands context

I spent the last 6 months building an AI secretary on WhatsApp, and the hardest part wasn't the AI — it was designing a system that actually understands context over time.

Here's what I learned about building AI agents that don't forget:

The Problem: State Management for Conversations

Most chatbots treat every message as independent. But real delegation requires memory:

• What did we discuss yesterday?
• What's the status of that task I mentioned?
• Who are the key people in my workflow?

The Solution: Context Layers

I ended up with 4 distinct memory layers:

1. Session context — what we're talking about right now
2. Short-term memory — recent tasks, pending items, active projects
3. Long-term profile — my preferences, common contacts, recurring workflows
4. External data — calendar, emails, docs that provide additional context

The Tech Stack That Worked

• Vector DB for semantic search across past conversations
• Structured storage for tasks/contacts/facts
• LLM with a carefully crafted prompt that weights each layer
• Webhook listeners for real-time updates

The Surprise Challenge

Context injection latency. When every reply needs to query 4 data sources, response time matters. I had to implement aggressive caching and async pre-fetching of likely-needed context.

The Result

An AI that can say: "You asked me to follow up with Sarah about the proposal. I see she replied 2 hours ago — want me to summarize her feedback and add it to your task list?"

That's the difference between a chatbot and a real assistant.

Has anyone else tackled multi-layer context management for AI agents? What worked for you?