The AI Agent Stack in 2026: What's Actually Working

Everyone is talking about AI agents, but what does a real production stack actually look like in 2026? I've been building and watching others build, and the patterns are starting to solidify.

The orchestration layer

Most teams are settling on one of three approaches: LangGraph for complex stateful workflows, bare-metal tool-calling with Claude or GPT-4o for simpler tasks, or n8n for anything that needs visual debugging and non-engineers to understand it. LangGraph wins on control; n8n wins on speed and accessibility.

Memory

Short-term: conversation context in the prompt. Medium-term: a vector DB (Pinecone or pgvector) for semantic retrieval. Long-term: structured summaries written back to a database after each session. Most teams underinvest in the long-term layer and wonder why their agents feel stateless after a week.

Tool connectivity

MCP is winning here. Not because it's perfect, but because it's standardized. Teams that built bespoke tool wrappers six months ago are quietly migrating to MCP servers. The connector ecosystem is growing fast enough that you rarely need to write from scratch.

Evaluation

Still the weakest link. Most teams are running vibes-based evals ('does it feel right?') or simple pass/fail unit tests. The teams doing this well are building golden datasets from production failures and running regression suites on every model update. Hard to do, but pays off immediately.

The honest take

The stack is maturing but not stable. If you committed hard to a framework six months ago, you're probably paying abstraction tax now. The teams winning are the ones keeping the core simple (direct API calls, minimal abstraction) and being deliberate about where they add complexity.

What does your current stack look like? Curious what's working - and what you've given up on.