Distributed Systems for AI
Connect traditional production software architecture with modern AI systems.
Why This Matters
Agents are long-running, unreliable, and side-effecting. Queues, sagas, idempotency, and backpressure are not optional once a tool can charge a card or write to a system of record.
Architecture Position
Enterprise Layer plus Runtime Layer: events, APIs, and durable workflows that outlive a single model call. See it on the architecture map.
Start Here
Core Concepts
Messaging
- Queues
- Streams
- Pub/sub
- Event buses
Workflow
- State machines
- Sagas
- Workflow engines
- Durable workflows
Reliability
- Idempotency
- Retries
- Backpressure
- Failure recovery
Data
- Data ownership
- Consistency
- Event sourcing
Coordination
- Distributed coordination
- Locks
- Long-running processes
AI Applications
- Event-driven agents
- Asynchronous agents
- Distributed tool execution
- Multi-agent coordination
Operations
- High availability
- Distributed tracing
- Scaling
- Failure domains
Advanced
Advanced material
Deeper guides for this domain are on the way.
Coming SoonArchitecture Patterns
Applied patterns for this domain live in the pattern library.
Browse architecture patternsCase Studies
Enterprise walkthroughs that apply this topic in a full system.
Browse case studiesRelated Topics
Agent Runtimes & Harnesses
The runtime as the boundary between probabilistic reasoning and deterministic enterprise execution.
Open →AI Platforms & Infrastructure
Infrastructure needed when AI moves from an app into an enterprise-wide capability.
Open →Evaluation, Observability & Reliability
How AI systems are measured, debugged, and operated safely in production.
Open →Enterprise AI Architecture & Governance
How these technologies become an enterprise platform and operating model.
Open →Roadmaps
This topic appears in the Enterprise AI Architect path.
Follow the Enterprise AI Architect roadmap