Most teams building with LLMs stop at the chat interface. A text box, a streaming response, maybe some retrieval-augmented generation bolted on. That's a feature, not architecture.
The shift happening now is from conversational AI to agentic AI: systems that plan, execute, observe, and correct autonomously within your product's domain. The difference matters because agents don't just answer questions. They perform work.
The Agentic Stack
After deploying several production agentic systems, the architecture pattern that holds up looks like this:
Orchestration layer. LangChain or a custom DAG runner that manages tool selection, state, and retry logic. The agent decides what to do next based on observation, not a fixed script.
Tool registry. Every action the agent can take is a typed, validated function. Database queries, API calls, file operations, Slack messages. Each tool has a schema the LLM uses to decide when and how to invoke it.
Memory and context. Vector store (Qdrant or pgvector) for semantic retrieval, plus a structured session store for conversation state. The agent needs both long-term knowledge and short-term working memory.
Guardrails. Output validation, cost limits, human-in-the-loop checkpoints. Production agents need circuit breakers. An agent that can execute business logic can also execute business logic incorrectly.
What Changes in Practice
The biggest shift is error handling. A chatbot that gives a wrong answer is annoying. An agent that executes the wrong action is a production incident. Every tool call needs to be idempotent or reversible. Every agent decision needs an audit trail.
The second shift is latency tolerance. Agents make multiple LLM calls per task: planning, execution, observation, correction. A single user action might trigger 5-10 API calls under the hood. You need async execution, status updates, and graceful degradation.
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Over-autonomy. Giving agents too much freedom before you've validated their decision-making in your specific domain. Start with narrow, well-defined workflows. Expand scope as you build confidence in the system's reliability.
Ignoring cost. A single agentic workflow can burn through tokens fast. Monitor cost per task, set hard limits, and optimize prompts aggressively. The difference between a well-prompted and poorly-prompted agent is often 10x in token consumption.
No observability. If you can't trace every decision an agent made and why, you can't debug it, improve it, or trust it. Log everything. Replay capability is not optional.
Where This Goes
The current generation of agentic systems is roughly where web apps were in 2005: functional but fragile, with a lot of convention still being figured out. The teams that invest in reliable, observable, well-bounded agent infrastructure now will have a significant advantage as the tooling matures.
The hard part isn't the AI. It's the engineering around the AI.