Practical guide | October 25, 2026 A new model can improve one task while changing the behavior your workflow depends on. Before switching an existing automation, test the whole job rather than relying on a benchmark headline. Save the current baseline Record the model configuration, prompt, tool permissions and representative outputs. Keep examples of past failures as well as ordinary tasks. Without a baseline, it is difficult to tell whether a new problem came from the model or another part of the workflow. Check the boundaries Test ambiguous instructions, unavailable sources and requests outside the intended scope. Verify that the system preserves uncertainty and stops for required approvals. If the agent can publish or send messages, use a test destination with harmless content during evaluation. Validate integrations Inspect structured fields, tool arguments and error handling. Check whether long inputs are truncated, whether citations support the text, and whether retries create ...
Practical guide | October 25, 2026 If an AI agent changes a document or sends information to another service, an incident reviewer needs to reconstruct the action. Logging everything indiscriminately can create a second problem: a new store of private content and credentials. Record the decision boundary Useful fields include the task identifier, acting identity, tool name, destination, time, permission decision and execution outcome. Where human approval is required, link the action to the approved version. Separate a proposed action from a completed one. Keep secrets out Redact authorization headers, tokens and unnecessary personal details. Review nested tool parameters and error messages, which may contain sensitive values even when the main prompt does not. Apply redaction before records reach shared dashboards or third-party monitoring services. Restrict the log itself Give reviewers access according to their responsibilities. Decide how long each class of record is needed, how it...