Building Multi-Agent AI Systems: Lessons from Cortex
What I learned building a visual multi-agent AI workspace with real-time streaming and task delegation.
The Problem with Single-Agent AI
Most AI interactions are straightforward: you prompt, it responds. But complex tasks benefit from specialized reasoning—research, coding, critique, creative ideation—handled by different "agents" with distinct capabilities.
The challenge? Making this orchestration visible and engaging rather than a black box.
Approach: Visual Agent Collaboration
With Cortex, I set out to build a system where you can literally watch agents think, delegate, and solve problems together.
Architecture Decisions
1. Real-time Streaming
Using Vercel AI SDK's streaming capabilities, each agent's reasoning appears token-by-token. This isn't just eye candy—it helps users understand how agents approach problems.
2. Agent Specialization
Rather than one general-purpose agent, Cortex uses specialists:
- Researcher: Gathers context and information
- Coder: Writes and reviews code
- Critic: Evaluates and improves outputs
- Creative: Generates ideas and alternatives
3. Visual Task Graph
Framer Motion powers an animated network visualization showing agent relationships and task flow. Users see delegation happen in real-time.
Results
- Built in 2 weeks as a proof of concept
- Demonstrates that multi-agent UX can be intuitive
- Open source for others to learn from
Key Takeaways
- Show the work: Users trust AI more when they can see reasoning
- Specialize agents: Focused roles outperform jack-of-all-trades
- Stream everything: Perceived latency matters as much as actual latency
Multi-agent systems are the future of AI interfaces. The key is making complexity comprehensible.