Systems for real work
AI agents with a narrow mandate and accountable controls
For workflows where software must interpret varied content, select among permitted actions, and use tools — without hiding permissions, uncertainty, or human responsibility.
Signs of a fit
- inputs arrive as messages, documents, or free text;
- rigid rules produce an unmanageable number of branches;
- the task can be expressed as bounded decisions and tools;
- errors are detectable, reversible, or safely escalated;
- representative examples exist for evaluation.
Example applications
Hypothetical examples include drafting a response for approval, triaging an inquiry and assembling context, extracting document fields with confidence checks, or proposing a next action without independently executing a consequential change.
Boundaries
An agent should not receive broad permissions or make irreversible decisions without controls. When a rule or integration can do the job, it is usually the better mechanism. Model capability does not repair poor source data or unclear accountability.
Delivery and client inputs
We define the task, allowed tools, data access, confidence thresholds, approval points, and failure scenarios. Evaluation happens on agreed examples before scope expands. Useful inputs include real-world variants, expected outcomes, access policies, and a named decision owner.
Related insight: AI agent or workflow automation?
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