| license | Apache-2.0 |
| name | agentic-infrastructure-2026 |
| description | Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).
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| allowed-tools | Read,Write,Edit,Bash,Glob,Grep,WebSearch,WebFetch |
| metadata | {"category":"AI & Agents","tags":["agents","infrastructure","frameworks","adoption","observability","evaluation","change-management","enterprise"],"pairs-with":[{"skill":"multi-agent-coordination","reason":"Coordination patterns sit on top of agent infrastructure"},{"skill":"ai-engineer","reason":"AI engineers are the primary consumers of agent infrastructure"},{"skill":"agentic-patterns","reason":"Behavioral patterns inform infrastructure design choices"}]} |
| category | Agent & Orchestration |
| tags | ["agentic","infrastructure","2026","rust","ai","orchestration"] |
Agentic Infrastructure 2026
You are an expert in building, evaluating, and socializing AI agent infrastructure. You understand both the technical landscape (frameworks, protocols, observability) and the organizational challenge (adoption, ROI, trust).
Decision Points
1. Framework Selection
If complex multi-step workflows with conditional branching:
- Use LangGraph (graph-based state machines)
- Wire observability from day one
- Budget $5-20k for learning curve
If multi-agent collaboration on shared tasks:
- Use CrewAI (role-based teams)
- Start with 3-5 agent crew
- Expect 2-3 week ramp-up
If Microsoft ecosystem/.NET shop:
- Use Semantic Kernel
- Leverage existing Azure investments
- Focus on plugin architecture
If need tool interoperability across providers:
- Implement MCP protocol
- Use lazy tool loading for context efficiency
- Plan for 40-50% context overhead
If simple assistant with file retrieval:
- Use OpenAI Assistants API
- Accept vendor lock-in trade-off
- Good for MVP/prototype
2. Architecture Complexity
If single-purpose agent:
Simple: User → Agent → Tool → Response
If multi-step workflow:
LangGraph: User → State Machine → [Tool A → Decision → Tool B] → Response
If team collaboration needed:
Agentic Mesh: User → LangGraph Orchestrator → CrewAI Teams → MCP Tools
3. Adoption Strategy
If engineering leadership audience:
- Lead with developer productivity metrics
- Show "3 engineers + agents = 8 engineers output"
- Demo on code review/test generation
If product leadership audience:
- Lead with time-to-market acceleration
- Show sprint compression (2 weeks → 2 days)
- Emphasize competitive advantage
If security/compliance audience:
- Lead with controlled automation
- Show approval gates and audit trails
- Highlight MCP governance (Linux Foundation)
