Articles
Give long-running agents a working account of the task New research on explicit belief states gives agents a way to track what remains unknown and recognise stalled progress. The opportunity is a more useful investigation workflow, with a measurable cost for maintaining that state. Choose analysis work with an independent check BootLoops gives AI agents a toolkit for exact and checkable quantitative work. Its useful design lesson is to choose analyses where an independent computation can verify the result, while a domain expert still decides whether the question matters. Hold out the customer before funding the rollout Fresh tickets from familiar accounts do not establish performance on unseen customers. Match the evaluation boundary to the proposed rollout and report the evidence for each population separately. Restore the AI service from a compatible record Approve an AI recovery plan against a restored service that completes useful work. Test document and index compatibility, reconcile current access permissions and state the recovery scope that the evidence supports. Test the questions your documents cannot answer Test how an assistant responds when its permitted evidence does not contain the answer. Separate unsupported answers from unnecessary refusals, and measure the human handoff before approving the service scope. Randomise the capacity pool before testing the AI When trial users compete for shared appointments, stock or staff, treatment can change the control group’s opportunities. Design the comparison around that resource before inferring the effect of a full rollout. Trace the shared dependency behind every AI fallback Accept an AI fallback against the work it can complete during a defined disruption. Map shared identity, retrieval, gateway and supplier dependencies, then test the minimum service and its operating limits. Test where an agent token stops working Measure when every receiving service rejects an agent’s credential after authority is withdrawn. Include cached checks, active sessions and queued work before accepting the access design. Give outcome labels time to mature Recent records may contain unresolved outcomes rather than negative results. Define the target window and reporting allowance before using conversion labels for retraining or investment decisions. Build reassessment into AI procurement An AI supplier can change the model, data handling or workflow after contract award. Define material-change triggers, evidence rights and review authority so continued use remains an explicit buyer decision. Put a decision gate on live AI testing A live AI trial needs a defined customer outcome, exposure boundary and executable stop decision. Use the proof of concept to design that trial, then use the trial to decide whether the service should expand. Constrain the inventory policy before training the agent An inventory agent acts on cash, stock and service levels. Define its permitted actions, comparison policy and fallback before asking it to optimise replenishment.
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