How to Build an AI Agent

Tool use, planning loops, memory - the agent architecture that works in production not just demos.

AI agents are the most powerful and most fragile LLM application pattern. A demo agent that browses the web and writes code looks impressive. A production agent that reliably executes multi-step workflows without hallucinating tool calls, running infinite loops, or causing irreversible actions requires intentional architecture. This guide covers the patterns that make agents reliable.

No fluff. Production-grade answers from engineers who ship AI into real products.

The Agent Architecture Decision: ReAct vs Plan-and-Execute

ReAct (Reasoning + Acting): the agent alternates between thinking and tool use in a loop. Simple to implement, works well for tasks that can be decomposed on the fly. Breaks for tasks requiring upfront planning or parallel execution. Plan-and-Execute: the agent first creates a plan (list of steps), then executes each step. Better for complex multi-step tasks, easier to inspect and debug, supports parallel execution of independent steps. The right choice for most production agents.

At Valletta Software, we focus on:

Tool design: narrow specific tools beat broad generic ones - one tool one job

Tool schemas: precise JSON schema with descriptions - the LLM reads these to decide when to call

Guardrails: output validation before any irreversible action - human approval for high-stakes steps

Memory: short-term (conversation history) mid-term (working memory in context) long-term (vector store)

Loop termination: max iterations hard limit plus semantic similarity stopping condition - never infinite loops

Error handling: tool call failures return error message to agent - agent can retry or ask for help

Observability: log every tool call input and output - debug production failures without reproducing locally

The Safety Patterns That Production Agents Require

Every agent that takes actions in the real world needs these. Non-negotiable.

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Confirmation step: before irreversible actions (send email delete record) require explicit confirmation

Scope restriction: agents can only call tools they are explicitly given - not arbitrary code execution

Input sanitization: validate and sanitize all data entering tool inputs - prompt injection is real

Dry run mode: test agent behavior without executing side effects - essential for development

Cost caps: per-session token and API call limits - agents in loops are expensive

Audit log: every agent action with timestamp user and result - required for enterprise and regulated use

Rollback: for every write action design the read-only equivalent - undo is a feature not an afterthought

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Read: OpenClaw Architecture

Build RAG pipelines, agents, and LLM integrations from day one

Ship AI features 3x faster with AI-native tooling and methodology

Deploy to production - not just Jupyter notebooks and prototypes

Evaluate output quality - hallucination detection, cost optimization, monitoring

How to Build an AI Agent - With Engineers Who Deploy Them in Production

Forget the hype. We make AI work in the real world.

Our engineers are trained in the latest AI tooling - Copilot, Claude Code, Cursor, LangChain, and vector databases - and use them daily to ship production AI features, not just prototypes.

Choose from a solo dev, mini team, or full squad. All powered by AI and ready to build from day one.

Lets keep it simple.

Our AI engineers use the OpenClaw/NemoClaw agentic framework to build production agents: narrow tools guardrails on irreversible actions audit logs per-session cost caps and dry-run testing. Built to run 24/7 not to demo once.

How we work