A visual guide to building reliable, scalable, and valuable AI agents, from initial concept to full-scale deployment.
"Could a smart intern do it?"
This is the most critical test for scoping an agent's task. If the job is too complex for a capable human intern, it's too ambitious for an initial AI agent. This principle grounds your project in reality and is the first step toward success.
Scope a realistic task using the "Smart Intern Test" and create 5-10 concrete examples to establish a baseline.
Write a detailed Standard Operating Procedure (SOP) describing how a human would do the job. This becomes the agent's blueprint.
Isolate the core reasoning task. Build a Minimum Viable Product prompt and validate it with mocked tools and tracing.
Replace mock functions with real tools that connect to APIs (e.g., Google, SQL, Web Search) and add memory for context.
Use observability tools like LangSmith. Measure performance on quality, cost, and latency. Evaluate the full reasoning trajectory.
Package the agent with Docker and deploy on Kubernetes. Implement feedback loops (HITL, user ratings) for continuous improvement.
The "brain" or cognitive engine. It interprets input, makes decisions, and generates responses. Choose a model like GPT-4o or Claude 3.5 Sonnet and set temperature to 0.0 for predictable behavior.
The "hands and eyes" that connect to the outside world (APIs, databases, search). The LLM's understanding of a tool comes entirely from its docstring—clear descriptions are critical.
Allows the agent to retain context from past interactions. Use `ConversationBufferMemory` for short chats or vector-backed memory for long-term, cross-session knowledge.
Simple pass/fail tests aren't enough. A robust evaluation strategy is essential for building reliable agents. You must move beyond subjective impressions to objective, measurable metrics that cover the full spectrum of agent performance.
A conceptual breakdown of where evaluation focus is spent, balancing output quality with process efficiency and operational costs.
As tasks become more complex, a single agent can become a bottleneck. LangGraph enables sophisticated multi-agent systems that are more reliable and scalable.
| Architecture | Description | Best For |
|---|---|---|
| Single Agent (ReAct) | A single LLM selects from a suite of tools in a loop. | Simple, focused tasks like Q&A with search. |
| Multi-Agent Supervisor | A central "supervisor" agent routes sub-tasks to specialized "worker" agents. | Complex tasks requiring diverse skills, like a research project. |
| Hierarchical Agent Teams | An extension where workers can also be supervisors, creating teams of teams. | Enterprise-scale workflows that mirror organizational structures. |