Showcase: Awesome Agentic Patterns — Production MCP configs, multi-agent topologies & developer rule blueprints

[Suprovo Mallick (DYNO) (DYNOSuprovo)]

⚡ Production AI Agent Playbook & Patterns

Hey AutoGen community!

Over the past months of deploying multi-agent topologies and integrating Model Context Protocol (MCP) tooling across engineering workflows, we observed recurring patterns around context window degradation, tool sprawl, and uncontrolled token consumption.

We open-sourced Awesome Agentic Patterns to document battle-tested architectural topologies, MCP server configs, and developer prompt blueprints.

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🏛️ The 4 Production Topologies Documented

1. Hierarchical Supervisor: Coordinator agent delegates to ephemeral, domain-specific subagents (e.g. read-only Research vs read/write Coding) to prevent context window dilution.
2. Peer-to-Peer Swarm (Handoff): Dynamic state transitions via structured tool calls.
3. Plan-and-Execute: Discrete planner -> workers -> independent verifier loop.
4. Evaluator-Optimizer (Critic-Actor): Continuous reflection against validation checklists before code execution.

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🔌 Standardized Model Context Protocol (MCP) Integrations

Standard configurations for:

• Filesystem (@modelcontextprotocol/server-filesystem)
• Git introspection (mcp-server-git)
• PostgreSQL & SQLite (@modelcontextprotocol/server-postgres, mcp-server-sqlite)

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🚀 Automated Workspace Setup Wizard

Includes an interactive CLI tool (scripts/setup_agent.py) that scans any repository, detects the language stack (Python, TypeScript, Go, Rust), and scaffolds .cursorrules, CLAUDE.md, and mcp.json automatically.

👉 Repository: https://github.com/DYNOSuprovo/awesome-agentic-patterns

We'd love to hear your feedback on multi-agent failure modes you've encountered in production! Contributions and PRs for additional topologies are very welcome.