How AI Agent works in practice
At runtime, an AI agent operates inside an execution harness that manages its system instructions, conversation history, tool definitions, and stopping conditions. When triggered by a user message, a webhook, or a scheduled cron job, the model evaluates the current context and decides whether to emit a final response or generate a structured tool call.
If the model calls a tool, such as running a SQL query, searching a vector index, or executing Python in a sandboxed container, the harness intercepts the request, validates the arguments against a schema, runs the function, and appends the output back into the agent's working context. The agent then inspects the observation and chooses its next action, repeating this reason-and-act cycle until the goal is complete or a step budget is exhausted.
For example, an Edinburgh fintech deploys a customer-onboarding KYB (Know Your Business) agent. When a small business applies for a merchant account, the agent queries Companies House APIs, pulls ultimate beneficial owner filings, cross-references sanctions lists, flags discrepancies in uploaded bank statements, and posts a structured risk summary into Slack for the compliance officer.
Why it matters and trade-offs
AI agents bridge the gap between natural-language intelligence and legacy enterprise software. Because an agent can read unstructured documents and call REST APIs or MCP servers, teams can automate cross-system workflows without waiting quarters for core banking or ERP vendors to ship native integrations.
However, deploying an AI agent into production requires treating the model as an untrusted insider with API credentials. Without strict least-privilege scoping, execution timeouts, and loop detection, a confused or prompt-injected agent can burn hundreds of pounds in API tokens in minutes or corrupt live records. High-consequence mutations, such as wiring funds or deleting customer data, must always sit behind deterministic policy checks or human-in-the-loop confirmation.