---
title: "Overview"
description: "Complete implementation of industry-standard agent patterns - model-agnostic, composable, and production-ready."
---

import { Card, CardGroup } from "@mintlify/components";

mcp-agent provides implementations for every pattern in [Anthropic's Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents), as well as the [OpenAI's Swarm](https://github.com/openai/swarm) pattern. Each pattern is model-agnostic, and exposed as an AugmentedLLM, making everything very composable.

<CardGroup cols={2}>
  <Card
    title="Parallel Workflow"
    href="/workflows/parallel"
    icon="arrows-split-up-and-left"
  >
    Execute multiple tasks simultaneously with intelligent result aggregation
    and conflict resolution.
  </Card>

{" "}

<Card title="Router Pattern" href="/workflows/router" icon="route">
  Intelligent task routing based on content analysis, user intent, and dynamic
  conditions.
</Card>

{" "}

<Card
  title="Intent Classifier"
  href="/workflows/intent-classifier"
  icon="brain"
>
  Advanced intent recognition with confidence scoring and hierarchical
  classification.
</Card>

{" "}

<Card
  title="Evaluator-Optimizer"
  href="/workflows/evaluator-optimizer"
  icon="arrows-rotate"
>
  Quality control with LLM-as-judge evaluation and iterative response
  refinement.
</Card>

{" "}

<Card title="Orchestrator" href="/workflows/orchestrator" icon="users">
  Complex multi-step workflows with dependency management and state
  coordination.
</Card>

  <Card title="Swarm Pattern" href="/workflows/swarm" icon="hexagon">
    OpenAI Swarm-compatible multi-agent handoffs with context preservation.
  </Card>
</CardGroup>

<Card>
  **Next Steps:** Explore individual workflow patterns to see detailed
  implementation examples and learn how to combine them for your specific use
  cases.
</Card>
