4 points | by raczekk 14 hours ago ago
1 comments
We recently built a production-grade multi-agent system for document analysis using Anthropic’s Model Context Protocol (MCP). The system orchestrates 15+ agents with dynamic tool usage and task delegation.
Here's the deep dive: https://deepsense.ai/blog/standardizing-ai-agent-integration...
Key insights:
1. When MCP works best:
- Multiple agents sharing tools/resources
- Dynamic tool orchestration needs
- Rapid prototypes that must scale to production
2. When MCP is overkill:
- Simple static API integrations
- Performance-critical apps needing sub-ms latency
- When direct SDK calls are clearer
3. Practical takeaways:
- Design APIs for LLMs, not humans (strict typing = fewer errors)
- Limit tool access per agent (reduced hallucinations + ~50% token savings)
We also uncovered real security pitfalls in production and saw how model-task matching (e.g. Haiku vs Sonnet) affects performance and cost.
What are your experiences?
We recently built a production-grade multi-agent system for document analysis using Anthropic’s Model Context Protocol (MCP). The system orchestrates 15+ agents with dynamic tool usage and task delegation.
Here's the deep dive: https://deepsense.ai/blog/standardizing-ai-agent-integration...
Key insights:
1. When MCP works best:
- Multiple agents sharing tools/resources
- Dynamic tool orchestration needs
- Rapid prototypes that must scale to production
2. When MCP is overkill:
- Simple static API integrations
- Performance-critical apps needing sub-ms latency
- When direct SDK calls are clearer
3. Practical takeaways:
- Design APIs for LLMs, not humans (strict typing = fewer errors)
- Limit tool access per agent (reduced hallucinations + ~50% token savings)
We also uncovered real security pitfalls in production and saw how model-task matching (e.g. Haiku vs Sonnet) affects performance and cost.
What are your experiences?