AI Agent Ecosystems
Agent Ecosystem Expert
Comprehensive reference for AI agent development, orchestration, infrastructure, and security.
Quick framework selection
| Scenario | Framework | Why |
|---|---|---|
| TypeScript teams | Mastra | TS-native, built-in memory, workflow engine |
| Complex stateful workflows | LangGraph | Graph-based state machines, checkpointing |
| Fastest prototyping | CrewAI or Agno | Role-based DSL |
| Enterprise .NET/Azure | Semantic Kernel | Microsoft-backed |
| OpenAI-native with safety rails | OpenAI Agents SDK | Lightweight, handoff model |
| Google Cloud / multi-framework | Google ADK | Python/TS/Java/Go SDKs, A2A |
| Deep MCP integration / OS access | Claude Agent SDK | 37 pre-built tools |
| AWS-native, model-driven simplicity | Strands Agents SDK | LLM controls the agent loop |
Orchestration patterns
| Task shape | Pattern |
|---|---|
| Sequential dependencies | Pipeline |
| Quality control needed | Supervisor |
| Parallelizable independent work | Swarm |
| High-stakes decisions | Consensus |
Agent security (2026)
Defense-in-depth
- Input validation — Filter and sanitize all external content
- Sandboxed tool execution — MicroVMs, gVisor, or container isolation
- Context-layer governance — Least privilege
- Runtime guardrails — LlamaFirewall (Meta, open-source)
Cost optimization
LLM API calls account for 70–85% of total agent operating costs. Key strategies:
- Model routing (saves 40–75%): Route each step to the cheapest model that meets quality.
- Prompt caching (saves 45–80%)
- Context management (saves ~72%)
- Token budgets: Per-request
max_tokens, per-task budgets, per-day/month caps.