AI agents for companies — one agent per use case, human review kept, EU-hosted execution.

An AI agent is software that performs a specific repeating task using a language model plus tool access (CRM, email, calendar, file store, knowledge base). It differs from a chatbot in that it acts — creates a task, sends a draft, files a ticket — instead of only answering. A serious enterprise agent has four properties: a narrow defined job, scoped access to exactly the tools it needs, a logged action trail, and a human-in-the-loop checkpoint on decisions that touch money, customers or personal data. Anything else is a demo.

(1) Sales — lead qualification, CRM enrichment, drafted first-touch emails for human approval. (2) Customer service — first-line triage, suggested replies queued for an agent, ticket summarisation. (3) Marketing — content brief generation, AI search citation monitoring, recurring report drafts. (4) Operations — meeting summarisation with action items into the right tool, invoice and document classification, internal knowledge-base Q&A. (5) Finance — accounts-payable extraction with human approval on every payment. Out of scope on day one: anything that closes deals, signs contracts or moves money without a human in the loop.

Execution runs on EU-hosted infrastructure. Each agent gets the minimum tool access for its job — no superuser keys, no shared credentials. Every action is logged with timestamp, input, output and the model version that produced it. Personal data flows are GDPR-compliant: no training on customer data without explicit consent, no cross-customer data sharing, deletion on request honoured to the underlying logs. Risk classification follows the EU AI Act — high-risk categories require formal documentation and are scoped accordingly. Penetration testing on each delivered agent before go-live.

Default rule: any decision that touches money, customers or personal data requires human review before the agent acts. Drafts are generated, queued and approved — not auto-sent. Confidence thresholds are explicit; below threshold, the agent escalates. The model that quietly removes the human checkpoint is the model that ships a public failure within a quarter. Hasty refuses implementations that ask for full autonomy on customer-facing decisions on day one — the cost of one wrong agent reply to a real customer is always higher than the labour the autonomy was supposed to save.

Monthly report on four lines per agent: hours returned per week per role (time saved on the task the agent does), error rate per run (false positives, hallucinated answers, escalations that should have been auto-handled), customer-facing incident count (any output that reached a customer and required correction), and adoption rate among the intended users. If hours returned don't beat operating cost within 90 days the agent is rescoped or retired — not kept on a slide deck.

Hasty audits the candidate use cases, the data flows they'd touch and the human-in-the-loop checkpoints required before any implementation starts. The call is structural — which one agent is most worth shipping first and what 'done' looks like — not a demo or a sales pitch.

Book an agent scoping call →See Hasty's brand page →