AI Architecture Patterns 101: Workflows, Agents, MCPs, and A2A Systems

Over the past few weeks, I've been exploring the world of AI architectures. Here's what I've learned about the foundations of modern AI systems.

Hi everyone! As an AI product manager who's been working with AI technologies, I've realized I needed to deepen my understanding of how these systems are actually built. Like many of you, I started with the basics - understanding different model types, fine-tuning approaches, and prompt engineering. But I kept hitting a wall when trying to design more complex AI solutions.

That's when I began exploring AI architecture patterns - the blueprints for building sophisticated AI systems that go beyond single-model applications. What's fascinating is how these models can be organized into different architectural patterns to create truly powerful systems. In this blog, I'll share my journey learning about four essential AI architecture patterns that are transforming how we design AI systems: AI Workflows, AI Agents, Model Context Protocol (MCP), and Agent-to-Agent (A2A) Collaboration.

This blog post covers:

• What I've Learned About AI Architecture Patterns

• AI Workflows - The AI Assembly Line

• AI Agents - The Self-Directed Problem Solvers

• Model Context Protocol (MCP) - The Universal Language for AI

• Agent-to-Agent (A2A) - Distributed Reasoning and Negotiation

• Choosing the Right Architecture Pattern

• Implementation Challenges to consider

• How I'm Thinking About Measuring Success

• What I've Learned and Where I'm Going Next

This post covers everything I've learned over the past few weeks, from basic definitions to practical implementation challenges. Whether you're just starting your AI journey or looking to level up your understanding of AI system design, I hope my experiences will help you on your path!

What I've Learned About AI Architecture Patterns

When I first started learning about AI, I thought it was all about individual models like GPT or DALL-E. But I've discovered that AI architecture patterns are actually reusable structural designs that provide frameworks for building complete AI systems. These patterns help organize components in specific ways to address different use cases and requirements.

What's been eye-opening for me is seeing how AI has evolved beyond simple models to complex systems. Understanding these architecture patterns has completely changed how I think about implementing AI effectively. Each pattern offers different trade-offs, and learning when to use each one has been a fascinating part of my journey.

1. AI Workflows: The AI Assembly Line

What They Are

Think of AI workflows as structured, linear pipelines that process data through a series of predefined steps. Like a factory production line, each step performs a specific task before passing results to the next step in the sequence.

Key Components

• Orchestration Layer: Tools like Airflow, Prefect, LangChain, or DSPy coordinate tasks

• Task Units: Microservices or serverless functions that perform specific operations

• Data Passing: Central state storage via Redis, message queues, or shared blob storage

• Monitoring: Centralized observability tools (Datadog, Prometheus) track execution

Key Characteristics

• Linear execution path with clear input/output specifications

• Deterministic behavior with consistent, predictable results

• Modular design allowing component reuse and replacement

• Straightforward testing and debugging due to the clear separation of concerns

Real-World Example: Customer Support Automation

1. Input: Customer submits a support ticket saying, "My app keeps crashing."

2. Step 1: The NLP model categorizes this as a "Technical Issue"

3. Step 2: Sentiment analysis determines it's a "High Priority" case

4. Step 3: AI generates a troubleshooting response with relevant help articles

5. Step 4: Human reviewer approves the response

6. Output: Automated email sent to customer with solution

Another example would be a PM tool that automates weekly report generation by:

• Fetching feature usage metrics from Amplitude

• Querying churn risk segments from an ML model

• Summarizing user pain points from support tickets using GPT

• Generating a product brief with actionable recommendations

When to Use

• For recurring, predictable tasks with clear steps

• When reliability and auditability are critical

• For batch processing that follows a standard pattern

• When you need tight control over the execution sequence

2. AI Agents: The Self-Directed Problem Solvers

What They Are

AI Agents are autonomous systems that pursue goals through planning, tool usage, and learning from experience. Unlike workflows, agents adapt their approach based on the current context and feedback.

Key Components

• Core Loop: Think → Plan → Act → Observe cycle that drives behavior

• Memory Layer: Short-term (conversation history) and long-term (vector database) storage

• Tool Use: API integration, function calling, and plugin capabilities

• Execution Environment: Containers or orchestrators (Modal, Ray, Beam) that manage runtime

How They Differ from Workflows

Real-World Example: GenAI PM Copilot

A PM assistant that helps write strategy documents by:

• Understanding product context and goals through conversation

• Planning document structure by analyzing past launches

• Pulling insights from dashboards, competitive benchmarks, and Jira tickets

• Adapting its recommendations based on user feedback

Another example is a Travel Planning Agent that:

• Searches multiple flight booking sites

• Compare prices and arrival times

• Check your calendar for conflicts

• Considers your airline preferences

• Books tare he best option

• Sends confirmation to your email

When to Use

• For complex, open-ended tasks requiring judgment

• When the solution path might change based on discoveries

• When personalization and contextual awareness matter

• For assistant-like applications that need to "think" like a human

3. Model Context Protocol (MCP): The Universal Language for AI

What It Is

Model Context Protocol (MCP) is an emerging standard designed to coordinate multiple AI models and agents, enabling them to work together seamlessly by sharing context and memory in a structured way.

Key Components

• Structured Format: Standardized message format for models to exchange information

• Context Sharing: A Mechanism for passing relevant context between models and agents

• Memory Management: System for maintaining and updating shared memory across interactions

• Action Specification: Clear definition of available actions and their parameters

Key Characteristics

• Reduces ambiguity in model-to-model communication

• Prevents context loss when transitioning between models

• Minimizes hallucinations by maintaining a consistent shared state

• Enables complex multi-model systems with less custom integration code

Real-World Example: Multi-Model Customer Support

A customer support system where:

• The initial classifier model categorizes the request

• Domain-specific expert model generates a response

• Safety model reviews the output for policy compliance

• All models share context about the user's history and current conversation

MCP ensures each model has the necessary context without repetition or corruption.

When to Use

• When building systems that require multiple specialized models to collaborate

• For applications where context and memory need to persist across different models

• When you need to ensure consistency across a chain of model interactions

• For complex agent systems that need standardized communication protocols

4. Agent-to-Agent (A2A): Distributed Reasoning and Negotiation

What It Is

A2A systems involve multiple specialized agents collaborating to solve problems through communication, debate, and consensus-building. These systems distribute cognitive load across different perspectives.

Key Components

• Agent Contexts: Each agent has its own memory, goals, and domain expertise

• Communication Protocol: Structured messaging format for inter-agent exchange

• Coordination Layer: Central controller or decentralized protocol for collaboration

• Decision Mechanisms: Methods for resolving disagreements and reaching consensus

The Team Approach

Real-World Examples

AI PM Team Collaboration Simulation:

• Agent 1 (Research PM): Explores user needs and market opportunities

• Agent 2 (Tech PM): Evaluates technical feasibility and architecture

• Agent 3 (Data PM): Suggests validation experiments and metrics

• All agents debate, negotiate trade-offs, and converge on a launch plan

Content Creation Team:

1. Research Agent: Gathers information from reliable sources

2. Writing Agent: Creates an initial draft based on research

3. SEO Agent: Optimizes content for search engines

4. Editor Agent: Reviews for grammar, style, and coherence

5. Quality Control Agent: Ensures all requirements are met

When to Use

• For complex decisions requiring multiple specialized perspectives

• When simulating stakeholder dynamics would be valuable

• For balanced evaluation of competing priorities

• When you need transparent reasoning about trade-offs

Choosing the Right Architecture Pattern

Selecting the appropriate architecture depends on your specific needs, resources, and goals:

Decision Framework:

• Task Predictability: Workflows for predictable tasks, Agents for variable ones

• Coordination Needs: Simple systems may use Workflows, while complex model interactions benefit from MCP

• Perspective Needs: When multiple viewpoints matter, A2A systems excel

• Development Resources: Workflows are simpler to build; MCP provides standards for more complex systems

Implementation Challenges to Consider

As I've researched these patterns more deeply, I've discovered that implementing them in real-world environments comes with unique challenges that practitioners frequently discuss:

For AI Workflows

When studying workflow systems, experts emphasize it's essential to:

• Build on existing orchestration tools (like Airflow and LangChain) rather than creating custom solutions

• Implement robust error handling (as workflows often fail completely if one step fails)

• Set up proper monitoring (you can't improve what you can't measure)

• Design for idempotence (allowing safe retries when steps fail)

For AI Agents

The literature on agent systems consistently highlights the importance of:

• Establishing clear constraints (as agents can easily go off-track without boundaries)

• Implementing usage limits (to prevent runaway API calls and associated costs)

• Thinking carefully about memory management (as what agents remember significantly affects their behavior)

• Creating fallback options (since agents can get stuck in loops or fail to progress)

For MCP Systems

When reading about connecting multiple models, experts stress:

• Standardizing communication formats (to prevent misinterpretation between models)

• Managing shared context effectively (as context can be easily corrupted in transmission)

• Defining clear handoff protocols (clarifying which model handles what, when)

• Monitoring for consistency issues (as models can drift from shared reality)

For A2A Systems

From case studies of multi-agent systems, I've found that successful implementations focus on:

• Communication protocols (as agents need structured ways to collaborate)

• Decision-making mechanisms (defining how agents reach agreement)

• Timeout implementations (preventing endless deliberation loops)

• Human oversight integration (for reviewing complex decisions)

While I haven't directly implemented these systems myself, these recurring challenges highlighted by practitioners provide valuable guardrails for anyone planning to build with these architectural patterns. In the future, I hope to put these insights into practice and share first-hand experiences with the specific technical hurdles encountered.

How I'm Thinking About Measuring Success

For each pattern I've studied, I've been thinking about how to evaluate its effectiveness:

For Workflow Systems

Based on case studies and industry benchmarks, successful workflow implementations typically measure:

• End-to-end processing time (total latency from input to output)

• Step-by-step reliability (success rate of individual components)

• Cost efficiency (resources consumed per execution)

• Performance bottlenecks (identifying steps that limit throughput)

For Agent Systems

The literature on effective agent implementations emphasizes measuring:

• Task completion rates (can the agent successfully achieve its goals?)

• User satisfaction metrics (do people find the agent helpful and intuitive?)

• Tool utilization efficiency (how effectively does the agent leverage available resources?)

• Learning curve (does performance improve with more interactions?)

For MCP Implementations

When evaluating Model Context Protocol approaches, experts recommend tracking:

• Context preservation accuracy (how reliably information transfers between models)

• Communication efficiency (overhead required for context sharing)

• Hallucination reduction (compared to non-MCP approaches)

• End-to-end system performance (holistic evaluation of the entire chain)

For A2A Systems

Research on multi-agent systems suggests measuring:

• Decision quality (compared to single-agent alternatives)

• Perspective diversity (quantifiable inclusion of different viewpoints)

• Time-to-consensus (efficiency of collaborative decision-making)

• Human evaluation scores (expert assessment of reasoning quality)

What I've Learned and Where I'm Going Next

Through my learning journey, I've discovered that these four architectural patterns form the foundation of modern AI systems:

1. Start with Workflows when you need reliability and predictability

2. Move to Agents when flexibility and autonomous reasoning are required

3. Apply MCP when you need multiple AI components to work together seamlessly

4. Explore A2A when complex problems benefit from multiple specialized perspectives

What's been most valuable for me is understanding when to apply each pattern. I've realized that:

• Simple, repeatable tasks are perfect for workflows

• Complex, ambiguous problems often need agents

• When combining multiple models, MCP prevents chaos

• For problems requiring diverse expertise, A2A can provide remarkable results

I've also learned that real-world AI systems often use hybrid approaches. For instance, you might have a workflow system with certain steps handled by agents, all coordinated through MCP standards.

The next step in my learning journey is applying these patterns to build prototype systems. I'm particularly interested in experimenting with agent-based systems for creative tasks and workflow systems for data processing.

As AI continues to evolve, I believe understanding these architectural patterns will be essential for anyone looking to build sophisticated AI systems. I'm excited to keep learning and sharing what I discover!

Have you worked with any of these AI architecture patterns? I'd love to hear about your experiences in the comments below!