What's the best backend for AI agents?

"Best" depends heavily on what you're building, but here's what's converging as the current (2026) consensus:

The architecture has standardized around four layers

• The stack is settling into: model → runtime → harness → agent — this pattern showed up independently across LangChain's Deep Agents reference architecture, product launches, and tutorials this year. The advice from most practitioners: pick a framework per layer rather than building from scratch.

Agent frameworks (the "harness" layer)

• OpenAI Agents SDK — fits teams building tightly scoped assistants or delegation-based workflows on OpenAI's model stack, especially for tool-driven apps with MCP-compatible interfaces. It doesn't handle durable execution natively, so it's commonly paired with Temporal or DBOS for workflow-level durability across restarts
• LangChain / LangGraph — the broadest ecosystem and easiest tool integration, good for rapid prototyping, but offers less structural control than graph-based systems as complexity grows
• CrewAI — role-based agent definition (each agent has a role, goal, backstory, tools) with hierarchical or sequential task orchestration Framework-agnostic observability (e.g., LangSmith) can sit on top of any of these without locking you into a specific orchestration layer

Data/infra layer underneath the agent

• Apache Kafka, PostgreSQL, OpenSearch, and ClickHouse are the recurring foundation for feeding agents high-quality, real-time context — without this layer, agents hit memory loss and latency bottlenecks regardless of how good the reasoning layer is Session/state storage is commonly SQLite, Redis, or a SQL-based store, depending on scale

Architectural principles that matter more than framework choice

• Build a semantic tool layer — expose capabilities like "get customer orders" rather than raw API/database calls, so you can swap backends without rewriting prompts or agent logic
• Read-heavy, write-careful — start with read-only access; when you do allow writes, use a queue-and-approve pattern where the agent proposes a change and a human or rule engine approves it before it executes
• Clean separation between the model layer and enterprise systems — this abstraction is what prevents "shadow AI" and keeps visibility into what agents are actually touching

For TypeScript specifically

• The ecosystem is converging at the library level (agents increasingly agree on similar library choices) but is still fragmented at the system level — service-to-service communication, deployment, observability, and local/production parity remain open problems without a single dominant answer

If you tell me more about your use case (internal tool automation vs. customer-facing agent, language/stack constraints, whether you need multi-agent orchestration), I can narrow this down further.