Most multi-agent demos fail for one reason: no evaluation layer.

If you’re building agentic AI, you’re not just shipping prompts—you’re shipping a system: tools, vector stores, memory, orchestration, and handoffs between agents. Without a clear way to test reliability, accuracy, and regressions, the “wow” disappears the moment users try real workflows.

In our next DDS session, we’ll break down:

• Multi-Agentic Systems: roles (planner/executor/critic), coordination, tool use, memory patterns
• MCP (Model Context Protocol): a clean way to standardize tool + data access across agent workflows
• Evaluations: how to measure groundedness, consistency, tool-call correctness, and RAG quality using repeatable tests

If you’re building agents, this is the missing layer that turns experiments into production-ready systems.