AI Automation & Agents

Teams handling repeated information tasks with clear inputs, approved data sources and a person responsible for reviewing important decisions. The useful starting point is a bounded workflow, rather than an agent expected to run an entire business.

These are examples of possible scopes, not client case studies or measured results. An enquiry workflow could classify messages and draft replies for approval. A document workflow could summarize approved material and flag records for review. An internal assistant could retrieve information from a defined knowledge source.

Whether an example fits depends on data quality, access permissions, accuracy requirements and the cost of a mistaken output.

1. Map the task, inputs, exceptions and review points.
2. Choose deterministic rules where they are sufficient and AI where interpretation is needed.
3. Connect approved tools with limited permissions and visible logs.
4. Evaluate outputs against representative examples and define fallback and escalation behavior.

Define what the system may read, what it may change and what always requires approval. Sensitive information, third-party model costs, latency and external API limits affect the design.

The delivery scope should include evaluation examples, failure handling and who monitors the workflow. High-impact decisions need appropriate human review; fluent output alone does not establish correctness.

When should we build an agent rather than a fixed workflow?

Use fixed rules when the sequence is predictable. Consider an agent when the task requires choosing among approved tools or steps and the result can be evaluated within clear boundaries.

Can it act without human approval?

That depends on the risk and agreed permissions. Drafting or classification may be suitable for automation; actions involving commitments, sensitive data or important decisions should have explicit review rules.