Patterns

Architecture Patterns

Proven patterns for building reliable agentic systems.

messaging

Fan-Out Message Distribution

Distribute events from a single MCP server to multiple downstream consumers for parallel processing.

integration

Anti-Corruption Layer

Protect your domain model from external system concepts by translating between MCP server data formats.

integration

Adapter Pattern for Legacy Systems

Wrap legacy systems with MCP-compatible adapters to integrate them into modern agent workflows.

integration

API Gateway Pattern for MCP

Route external API requests through a unified gateway that dispatches to the appropriate MCP server.

deployment

Feature Flag Controlled Rollout

Use feature flags to control which users see new functionality without deploying new code.

deployment

Canary Deployment with Progressive Rollout

Gradually shift traffic to a new version while monitoring error rates and performance metrics.

deployment

Blue-Green Deployment with MCP Validation

Deploy new versions alongside old ones and switch traffic after MCP-driven validation passes.

resilience

Fallback with Graceful Degradation

Provide fallback responses when MCP servers are unavailable to maintain partial functionality.

resilience

Bulkhead Isolation

Isolate MCP server connections into separate pools to prevent one failing server from consuming all resources.

resilience

Retry with Exponential Backoff

Automatically retry failed MCP server calls with increasing delays to handle transient failures.

resilience

Circuit Breaker for MCP Servers

Prevent cascading failures by stopping calls to unhealthy MCP servers until they recover.

ai-agent

Agent Memory and Context Management

Manage long-term and short-term memory for AI agents using MCP servers for persistent storage.

ai-agent

Tool Selection with Confidence Scoring

Dynamically select the best MCP server tool for a task based on confidence scores and past performance.

ai-agent

Multi-Agent Coordination

Coordinate multiple specialized AI agents that each use different MCP servers for their domain expertise.

ai-agent

Retrieval-Augmented Generation (RAG)

Enhance AI responses by retrieving relevant context from databases and documents before generating.

monitoring

Alerting Cascade with Escalation

Route alerts through escalating severity levels with automatic escalation when not acknowledged.

monitoring

Distributed Tracing for MCP Calls

Trace requests across multiple MCP server calls with span context propagation.

monitoring

Centralized Logging Pipeline

Route all MCP server logs through a centralized pipeline for unified search and analysis.

monitoring

Health Check Aggregation

Aggregate health status from all MCP servers into a unified system health dashboard.

security

RBAC Middleware for MCP Servers

Implement role-based access control as middleware that gates which MCP server tools a user can invoke.

security

Secret Rotation Pattern

Automatically rotate secrets and credentials used by MCP servers without downtime.

security

Zero-Trust MCP Authentication

Verify every MCP server request with token validation, scope checking, and audit logging.

caching

Distributed Cache with Invalidation

Manage cache across multiple nodes with event-driven invalidation to prevent stale data.

caching

Write-Through Cache

Write to both cache and database simultaneously to keep them in sync at all times.

caching

Cache-Aside (Lazy Loading)

Load data into cache on demand: check cache first, fall back to database, then populate cache.

orchestration

Parallel Execution with Aggregation

Execute multiple MCP server calls in parallel and aggregate results into a unified response.

orchestration

Choreography-Based Coordination

Let MCP servers coordinate through events without a central orchestrator, enabling loose coupling.

orchestration

Workflow Orchestration with State Machine

Model complex business workflows as state machines with MCP servers handling transitions.

orchestration

Chain of Responsibility

Pass requests through a chain of MCP server handlers where each can process or forward to the next.

orchestration

Saga Pattern for Distributed Transactions

Coordinate multi-step transactions across MCP servers with compensating actions for rollback.

data-pipeline

Batch Processing with Parallel Workers

Process large datasets efficiently by dividing work across parallel worker agents with shared progress tracking.

data-pipeline

Change Data Capture (CDC)

Capture and propagate database changes in real-time to downstream systems using log-based CDC.

data-pipeline

Data Lake Ingestion Pattern

Ingest data from multiple sources into a centralized data lake with schema validation and partitioning.

data-pipeline

Streaming Data Pipeline

Process data in real-time as it flows through a series of transformation stages connected by MCP servers.

data-pipeline

ETL Pipeline with Checkpointing

Build resumable ETL pipelines that can recover from failures without reprocessing already-completed data.

messaging

Request-Reply over MCP

Implement synchronous request-reply communication between MCP servers using correlation IDs.

messaging

Message Queue with Dead Letter Handling

Process messages reliably with automatic retry and dead letter queues for failed messages.

messaging

Pub-Sub with Topic Routing

Route messages to specific MCP server consumers based on topic patterns for selective event handling.

messaging

Event Sourcing with MCP

Store all state changes as immutable events and rebuild state by replaying the event log.

Parallel Processing

Fan-Out / Fan-In

Distribute work across multiple parallel workers, then aggregate results. Ideal for tasks that can be broken into independent subtasks.

Data Processing

Sequential Pipeline

A linear chain of processing steps where each step's output becomes the next step's input. The simplest pattern for ordered, dependent operations.

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