Guide · AI Agents
How to Build AI Agents for Enterprise
An AI agent is more than a chatbot. It is a system that reasons over a goal, decides what actions to take, executes those actions using tools, observes the results, and iterates until the goal is achieved. Building agents that work reliably in enterprise production environments requires a different approach than building demos.
What an AI agent actually is
The term "AI agent" is overloaded. Marketing uses it for any LLM-powered feature. In engineering terms, an agent is specifically a system where an LLM serves as a reasoning engine that drives an action loop. The canonical definition has four components: an LLM that reasons and decides, tools that the agent can invoke to take actions in the world, memory that persists state across steps, and an orchestration loop that runs until a termination condition is met.
What distinguishes an agent from a simple chatbot or chain is autonomous action selection. A chatbot generates a response to a message. An agent decides what tool to call, calls it, reads the result, decides what to do next, and continues until the task is complete — without a human steering each step. This autonomy is what creates leverage. It is also what creates risk. An agent that makes a wrong decision early in a task can propagate that error through many subsequent steps before the failure is visible.
The practical definition that matters for enterprise deployment is narrower: an AI agent is a system that can reliably complete a defined class of tasks within a defined set of constraints, with observable behavior and controllable failure modes. The research definition — fully autonomous general-purpose agents that pursue open-ended goals — is not what enterprise deployments need or should be building toward in 2024.
Agent patterns
Different task structures require different orchestration patterns. Using the wrong pattern for your use case is one of the most common sources of agent failures in production. The three patterns you need to understand are ReAct, Plan-and-Execute, and multi-agent systems.
ReAct (Reason + Act)
ReAct is the foundational agent pattern, introduced in a 2022 paper by Yao et al. The agent alternates between reasoning steps (producing a thought about what to do) and action steps (calling a tool). After each tool call, the result is added to the context and the agent reasons again. This cycle continues until the agent produces a final answer or hits a step limit.
ReAct works well for tasks that are inherently sequential — where you cannot know what step three is until you see the result of step two. It is the default pattern in LangChain's agent executors. The limitation is that it is greedy: the agent commits to each action before seeing the full task, which means it can get stuck in dead ends or take unnecessarily roundabout paths on complex tasks. For tasks involving more than five or six sequential steps, performance degrades noticeably.
Plan-and-Execute
Plan-and-Execute separates planning from execution. A planner LLM call produces a complete task plan — an ordered list of steps — before any tools are invoked. An executor then works through the plan step by step, potentially using a different (often cheaper) model for execution. The plan can be revised mid-execution if a step fails or produces an unexpected result.
This pattern works better than ReAct on complex multi-step tasks because the planning step forces the model to reason about the full task before committing to actions. It is also more efficient: the planner can identify steps that can run in parallel, and the executor can use a smaller model where reasoning is not required. The tradeoff is that planning adds latency and the plan can be wrong, requiring replanning loops that add further latency.
Multi-agent systems
Multi-agent systems decompose a complex goal across multiple specialized agents coordinated by an orchestrator. A common pattern is the supervisor architecture: an orchestrator agent receives a high-level goal, decomposes it into sub-tasks, assigns each sub-task to a specialized worker agent, and synthesizes the results. Worker agents can be specialists — a research agent, a writing agent, a code execution agent, a validation agent — each with a tailored system prompt and tool set.
Multi-agent systems shine when a task naturally decomposes into parallel workstreams that can run simultaneously, or when different subtasks require genuinely different capabilities or tools. They are more complex to build, debug, and operate than single-agent systems. Do not reach for multi-agent architectures because they sound sophisticated. Reach for them when a single-agent system has demonstrably hit a capability ceiling.
Tools: how agents take actions
A tool is any function the agent can call. The model generates a structured tool call — a function name and arguments — and the framework executes it, returning the result to the model. Tools are what give agents the ability to act in the world rather than just generate text.
Function calling
Modern LLMs — GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro — support native function calling via structured outputs. The model is given a list of available functions with their JSON Schema definitions and returns a JSON object specifying which function to call and with what arguments. This is more reliable than parsing tool calls from free-form text. Well-designed tools have clear, unambiguous names and parameter descriptions. The model's tool selection quality degrades noticeably when tools have overlapping responsibilities or vague descriptions.
MCP servers
The Model Context Protocol (MCP), introduced by Anthropic, standardizes how agents connect to external systems. An MCP server exposes a set of tools over a defined protocol, and agents can connect to multiple MCP servers to expand their capabilities. This is useful for enterprise deployments where tools need to be versioned, access-controlled, and maintained independently of the agent itself. MCP enables a clean separation between the agent's reasoning logic and the tool implementations.
Web search and retrieval
Agents that need current information use web search tools. The agent generates a query, the tool fetches search results, and the agent reasons over the returned content. For internal knowledge retrieval, a vector database tool wired to a RAG pipeline serves the same function — letting the agent pull relevant documents on demand rather than having everything pre-loaded into context.
Code execution
Code execution tools give agents the ability to write and run code, which is a significant capability multiplier for data analysis, computation, and automation tasks. The agent writes Python or JavaScript, the tool executes it in a sandboxed environment, and the agent reads the output. Security is critical here: code execution must run in a fully isolated environment with no access to production systems, with strict resource limits to prevent runaway processes.
Memory types
Memory determines what the agent knows and can reference at any point during execution. Agents without adequate memory either hallucinate or repeat themselves. Agents with poorly designed memory become slow and expensive as context windows fill up. The right memory architecture depends on your agent's task structure.
In-context memory
Everything in the current conversation window — the system prompt, the conversation history, retrieved documents, tool call results, intermediate reasoning steps — constitutes in-context memory. It is the fastest and most reliable form of memory because the model attends to it directly. The constraint is context window size and cost: large in-context memories are expensive and can degrade attention quality on long conversations. Effective in-context memory management involves summarizing older turns, pruning irrelevant history, and loading only what is needed for the current step.
Episodic memory
Episodic memory stores records of past interactions or task executions that can be retrieved and used in future sessions. This enables an agent to remember that a user prefers a particular output format, that a specific API call failed last week, or that a recurring task has a known edge case. Episodic memory is typically implemented as a vector store where past interaction summaries are embedded and retrieved by semantic similarity to the current task. LangMem and MemGPT are frameworks specifically designed for agent episodic memory.
Semantic memory
Semantic memory is the agent's general knowledge store — the RAG pipeline, the knowledge graph, the database of facts about the world or your organization. Unlike episodic memory, which records events, semantic memory stores entities, relationships, and facts that persist independently of any specific interaction. For enterprise agents, semantic memory is often the primary knowledge source — the product catalog, the policy library, the customer records that the agent reasons over.
Frameworks: LangGraph, CrewAI, AutoGen
The framework you choose shapes how you express agent logic, how you debug it, and how much infrastructure you are responsible for. There is no universally correct choice — each framework has a different design philosophy and is optimized for a different set of use cases.
LangGraph models agent logic as a directed graph where nodes are agent steps and edges represent transitions. It provides first-class support for cycles, branching, parallelism, and persistent state across steps. It is the best choice for complex single-agent workflows and supervisor-worker multi-agent architectures. The explicit graph structure makes it easier to reason about control flow and debug failures than implicit chain-of-thought patterns.
CrewAI uses a role-based abstraction: you define agents as crew members with a role, goal, and backstory, then orchestrate them in processes (sequential or hierarchical). It is more accessible than LangGraph for teams new to multi-agent systems and produces good results for creative and research-style workflows. For strict enterprise requirements — deterministic routing, fine-grained observability, complex state management — LangGraph is typically more appropriate.
Microsoft's AutoGen v0.4 introduces an actor model for multi-agent systems with a strong emphasis on asynchronous, event-driven agent communication. AutoGen Studio provides a visual interface for prototyping multi-agent workflows. It is well-suited for research and experimentation and for teams already in the Microsoft ecosystem. The framework has seen significant architectural changes between versions, so production adoption requires careful version pinning.
Production concerns: what kills agents in the real world
The gap between a working agent demo and a production agent system is larger than in conventional software. Agents fail in ways that are difficult to reproduce, difficult to attribute, and difficult to fix without understanding the full execution trace. These are the production concerns that matter most.
Observability
Every LLM call, every tool call, every intermediate reasoning step must be logged with timestamps, token counts, and structured output. LangSmith (LangChain's tracing platform), Arize Phoenix, and Weights & Biases provide agent-specific observability tooling. Without traces, you cannot diagnose why an agent failed on a specific input or identify the systematic failure modes that appear across many runs.
Rate limits and retries
Agents that make many LLM and tool calls per task will hit API rate limits. Exponential backoff with jitter, token-based rate limiting at the application layer, and fallback model routing (switch from GPT-4o to GPT-4o-mini when rate limited) are all production requirements. An agent that crashes on a rate limit error rather than retrying gracefully is not production-ready.
Fallbacks and error recovery
External tools fail. APIs return unexpected formats. Models produce malformed JSON. A production agent must handle these failures gracefully — retrying transient failures, surfacing tool errors to the model so it can adapt, and falling back to a degraded-but-functional mode rather than crashing. Every tool call should have a defined error contract and every agent should have a maximum step budget to prevent infinite loops.
Human-in-the-loop
For high-stakes actions — sending an email to a customer, executing a financial transaction, deleting a record, publishing content — agents should pause and request human approval before proceeding. LangGraph supports interrupt nodes that halt execution and wait for a human decision before continuing. Designing the right human-in-the-loop checkpoints is a product decision as much as an engineering one: too many interrupts defeats the purpose of automation; too few exposes you to unacceptable risk.
We build enterprise AI agents.
Architecture, tool selection, orchestration, observability, and production deployment. We design and ship agents that work reliably and hold up under real operational demands — not just in demos.
