AI agents are no longer a research curiosity — they're running in production, handling customer support, executing multi-step workflows, and making decisions that affect real business outcomes. The question is no longer whether to build agents, but how to build them so they don't collapse under real-world conditions.
Most agent tutorials get you to a working demo in 30 minutes. Almost none of them tell you what happens when that agent runs 10,000 times a day, encounters edge cases, or needs to be debugged at 2am. This post covers the architecture decisions that determine whether your agent stays working — or becomes a liability.
What an AI Agent Actually Is
Strip away the hype and an AI agent is a loop: an LLM receives a task, decides what action to take, executes that action (often via a tool or API call), observes the result, and repeats until the task is complete or a stopping condition is met.
The power comes from the tools — web search, database queries, code execution, API calls, file operations. The risk comes from the same place: agents with broad tool access and unclear stopping conditions can do a lot of damage fast.
Pattern 1: ReAct (Reason + Act)
The most widely used agent pattern. The model alternates between reasoning steps ("I need to look up the customer's account balance") and action steps (calling the accounts API). Each reasoning step is explicit and logged, which makes debugging tractable.
When to use it: General-purpose task agents, research agents, support automation. Good default for most use cases.
Watch out for: Reasoning loops — the model can get stuck cycling through the same reasoning steps without making progress. Always implement a max-step limit.
Pattern 2: Plan-and-Execute
A planner LLM breaks the task into a structured sequence of subtasks, then an executor LLM (often smaller and cheaper) carries out each step. The plan can be reviewed — or even edited — before execution begins.
When to use it: Complex multi-step workflows where you want human review before the agent acts. Document processing, compliance workflows, anything with significant downstream consequences.
Watch out for: Plan rigidity. If an early step produces unexpected output, a rigid plan executor will fail. Build in replanning logic for when steps deviate from expectations.
Pattern 3: Multi-Agent Orchestration
Break your agent into specialized sub-agents — a researcher, a writer, a fact-checker, a formatter — orchestrated by a supervisor agent that routes tasks and aggregates results.
When to use it: Tasks that naturally decompose into parallel workstreams. Content pipelines, data enrichment workflows, complex analysis tasks.
Watch out for: Communication overhead and error propagation. When a sub-agent fails, the orchestrator needs a clear strategy — retry, skip, escalate, or abort. Define this before you build.
The Four Things Every Production Agent Needs
Regardless of which pattern you choose, these four components separate demo agents from production agents:
1. Structured tool definitions. Every tool your agent can call should have a typed schema with clear descriptions. Ambiguous tool definitions are the #1 cause of agents calling the wrong tool or passing malformed arguments. Use Pydantic, JSON Schema, or your framework's equivalent — and test each tool in isolation.
2. Memory architecture. Decide upfront what your agent remembers and for how long. Short-term memory (within a session) is usually handled by the context window. Long-term memory requires a retrieval system — a vector store, a structured database, or both. Agents without well-designed memory either repeat work they've already done or hallucinate facts they should have looked up.
3. Observability from day one. Every step your agent takes should be logged: the input, the reasoning, the tool called, the tool output, the final response, and the latency of each. Without this, debugging a failed agent run is nearly impossible. OpenTelemetry, LangSmith, or a custom logging layer — pick one before you go live.
4. Failure handling and guardrails. Define what happens when a tool call fails, when the LLM returns malformed output, or when the agent exceeds its step budget. Agents that fail silently are worse than agents that fail loudly. Build explicit error states and escalation paths — including fallback to human review for high-stakes decisions.
Start Narrow, Then Expand
The most successful agent deployments we've seen start with a tightly scoped task — one tool, one workflow, one type of input — and expand from there. The temptation is to build a general-purpose agent that can do everything. Resist it. A narrow agent that works reliably is worth ten general agents that fail unpredictably.
Get the first agent right. Understand its failure modes. Then add capabilities deliberately, testing each addition against your production criteria before it goes live.