---
title: "AI Agent Ecosystems"
description: "Comprehensive reference for AI agent development, orchestration, infrastructure, and security."
---

# 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
1. **Input validation** — Filter and sanitize all external content
2. **Sandboxed tool execution** — MicroVMs, gVisor, or container isolation
3. **Context-layer governance** — Least privilege
4. **Runtime guardrails** — LlamaFirewall (Meta, open-source)

## Cost optimization

LLM API calls account for 70–85% of total agent operating costs. Key strategies:
1. **Model routing (saves 40–75%):** Route each step to the cheapest model that meets quality.
2. **Prompt caching (saves 45–80%)**
3. **Context management (saves ~72%)**
4. **Token budgets:** Per-request `max_tokens`, per-task budgets, per-day/month caps.