How We're Solving Context Engineering for AI Agents at JustCopy.ai

Building smarter AI agents through better prompt architecture and dynamic context management

Hey Everyone! 👋

I’ve been deep in the trenches building AI agents at JustCopy.ai, and I want to share one of the most critical (yet underrated) challenges we’ve been tackling: context engineering.

The Problem: Context Overload vs. Context Starvation

If you’re building AI agents, you’ve probably hit this wall: give your agent too much context, and you waste tokens, increase latency, and dilute relevance. Give it too little, and it makes brittle decisions that break in edge cases.

This isn’t just about prompt engineering anymore. It’s about dynamic context management at scale.

Our Approach: Layered Context Architecture

We’ve developed a three-tier system that’s working remarkably well:

1. Static Foundation Layer

The core identity, capabilities, and operational guidelines of the agent. This rarely changes and forms the bedrock of every interaction. Think of it as the agent’s “personality” and fundamental operating system.

2. Dynamic Session Context

User-specific information, conversation history, and task state that updates throughout an interaction. This is where we implement smart windowing - keeping only the most relevant recent context and summarized historical state.

3. Just-In-Time Retrieval Layer

This is the game changer. Instead of front-loading everything, we pull in relevant context dynamically based on the agent’s current task. We use a combination of vector similarity search and rule-based triggers to inject exactly what’s needed, when it’s needed.

Technical Details

Our retrieval system uses embeddings to maintain a semantic memory bank. When an agent needs to make a decision, we:

• Compute embeddings for the current context

• Query our vector store for relevant historical patterns

• Apply a relevance threshold (we found 0.75 cosine similarity works well)

• Inject only the top-k results (usually k=3-5) into the working context

We also implement aggressive context pruning. Every 5-7 turns, we summarize the conversation state and compress older context. This keeps token counts manageable while preserving semantic continuity.

The Results

• 40% reduction in average token usage per interaction

• 2x improvement in handling edge cases (measured by successful task completion)

• 60% faster response times due to smaller context windows

• Better agent reliability - fewer hallucinations, more consistent behavior

What We’re Still Figuring Out

1. Optimal compression strategies for different domain types

2. When to prioritize recency vs. relevance in context selection

3. How to handle multi-modal context (text, code, structured data) efficiently

4. Building better debugging tools for context state inspection

Why This Matters

As AI agents become more autonomous and long-running, context engineering will become as critical as traditional systems architecture. We can’t just throw unlimited context at models - we need intelligent, dynamic context management systems.

This is infrastructure work. It’s not sexy, but it’s essential.

I’m sharing this because:

1. I wish more teams would open up about these architectural challenges

2. I’d love feedback from others solving similar problems

If you’re working on AI agents and dealing with context engineering challenges, I’d love to hear:

• What approaches have worked (or failed spectacularly) for you?

• How are you handling context compression and retrieval?

• What tools are you using to debug context issues?

• Are you seeing similar performance improvements with dynamic context management?

Drop your thoughts in the comments or reach out directly. Let’s figure this out together.

And if you’re passionate about building robust AI agent infrastructure and want to work on problems like this. Would love to chat.

Happy to answer any technical questions in the comments!

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P.S. - I’ll be monitoring this thread actively. Hit me with your toughest context engineering questions. 🚀