How I Built AI Agents for Enterprise Automation

Enterprise back-office work contains enormous quantities of repetitive, information-dense tasks: researching a supplier before a sourcing event, enriching a contact record with publicly available information, checking a document against a compliance checklist, summarising a set of meeting notes into action items. These tasks are not complex enough to require human judgement for every instance, but they are too variable for traditional automation — rule-based RPA breaks on edge cases, and there are always edge cases.

The AI Agents and Automation Tools built for this context use language model-based agents to handle the variable, information-dense work that sits in the gap between human judgement tasks and simple automation. The agents research, enrich, classify, and summarise — operating across multiple sources and steps — with human review at defined checkpoints rather than at every step.

Architecture

The orchestration layer is built on LangGraph, which provides a graph-based state machine for managing multi-step agent workflows. LangGraph was chosen over simpler agent frameworks because it allows explicit control of the execution flow — you define the nodes (steps) and edges (transitions) of the workflow, rather than relying on the model to decide what to do next at every point. This makes the system more predictable and easier to debug.

Each agent has a defined set of tools: web search, document retrieval from internal stores, structured data lookup via API, and output formatting. OpenAI models power the reasoning and synthesis steps. n8n handles scheduling and integration — triggering agents on a cadence, routing outputs to the right downstream systems (Slack notifications, database updates, email summaries), and managing the human review checkpoints where an agent output requires approval before proceeding.

The Reliability Engineering Problem

The hardest part of building production-grade AI agents is not the intelligence — it is the reliability. Early versions of these agents failed unpredictably: the model would call a tool in the wrong format, or produce an output that did not match the expected schema, or enter a loop when a tool returned an unexpected response. Each failure required manual intervention to restart the workflow.

The reliability improvements came from three sources. First, guardrails: explicit validation of tool call formats and output schemas before the result is accepted, with retry logic for malformed outputs. Second, scope limitation: each agent does one thing well rather than many things approximately. Third, observability: full logging of every step, every tool call, and every output makes it possible to identify where failures occur and what caused them. The current system handles edge cases gracefully and alerts human reviewers when it encounters a situation outside its configured scope.

Capabilities and Outcomes

The deployed agents handle supplier research (publicly available financial, operational, and risk information compiled into a structured brief), contact enrichment (job title, company information, and recent activity appended to CRM records), and document review (compliance checklist checking against uploaded policy documents). Each workflow runs on a cadence or is triggered by a specific event. Human review is built into workflows where the output informs a significant decision. Processing time for tasks that previously took thirty to sixty minutes of analyst time is now under five minutes per batch, with accuracy validated against a sample of manually reviewed outputs.