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A reduced-function mode used when a dependency is unavailable but the workflow can still provide partial value safely — show prior summaries, disable write actions.

A design where the system continues when a check fails. Sometimes acceptable for low-stakes actions; dangerous for regulated or write-capable workflows.

The way a deployment detects, protects, minimizes, logs, or avoids personally identifiable information — must be approved before real customer data enters any eval set.

A validation step that confirms output, action, or data access complies with customer policy or product rules before the workflow continues.

A review of deployment risks — data exposure, bad actions, reliability, compliance, user misuse, and operational failure modes — often what changes a full write-back launch into an approval-only one.

A deliberately adversarial or high-risk test case used to probe model, agent, security, or workflow weaknesses before production.

A failure mode where a user, prompt, tool, or configuration avoids a control that was supposed to limit behavior. Treated as a launch-blocking bug.

A deliberate manual override or alternate path that lets operators recover when automation, data, or integrations fail — should be designed in, not bolted on after launch.

The designed path when automation cannot proceed safely — routing to a human, using a simpler workflow, or reverting to manual processing.

A threshold used to decide when model or agent output is good enough to proceed, requires human review, or should fall back to a simpler path.

A required approval step before a workflow can proceed or a system action can be taken.

A specific point in a workflow where a human must review, approve, resolve ambiguity, or accept responsibility before the workflow continues.

A connector that exposes a specific system, API, or data source through MCP so an AI application can use it safely with standard tool calling.

The controlled integration layer that exposes customer tools, data, or actions to AI applications through MCP servers — enforcing permissions without baking them into every agent.

A server implementing the Model Context Protocol so AI systems can access tools, resources, or prompts through a standard interface — the preferred integration layer for FDEs building agent connections to customer systems.

A record of which tools an agent called, with inputs, outputs, timing, and errors — the primary debugging artifact for agentic workflows.

A safe environment where agent tool calls can be tested without touching production data or committing real actions.

A governed inventory of tools available to agents or applications — ownership, scopes, descriptions, and approval status tracked so nothing undocumented gets used in production.

The agreed inputs, outputs, permissions, errors, and side effects of a tool an agent or workflow can call. Makes clear what the agent can do, not just what it can call.

A signal generated when users correct, reject, or bypass system output — one of the most valuable inputs for improving evals and workflow design.