Move beyond the pilot
A prototype proves that an idea can work once. Production requires it to work repeatedly—under changing data, real permissions, edge cases, and business pressure.
Design for the real world
Start with practical guidance on evaluation, security, reliability, monitoring, and the architecture that keeps AI useful after launch.
Salesforce published fourteen months of its customers' agent data and the escalation rate to humans held at 32%. Here is how we read that number, and the four questions we answer before any task type gets to run without a person.
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Download the whitepaper to learn how leaders can assess AI readiness, prepare high-value workflows, and move Operational AI into production.
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AI workflow testing should cover golden cases, risky edge cases, prompt-injection attempts, reviewer edits, regressions, and live performance metrics.
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Prompt injection risk rises when agents read untrusted context and can call tools, so defenses must combine input handling, permissions, approval, and logging.
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Human-in-the-loop AI works when the wait rules are explicit: risk, reversibility, uncertainty, data sensitivity, customer impact, and exception status.
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Before a pilot becomes a production AI system, require proof across context, control, evaluation, monitoring, and operating ownership.
Read the guideReady to act?
Diagnose the missing operating layer, plan a bounded implementation, or build the system for continuous measurement and improvement.
Identify the context, workflow, control, and operating gaps keeping your pilot from production.
Learn more →Measure, govern, and improve AI workflows as models, data, and operating conditions change.
Learn more →Take a bounded agentic workflow from production design through launch and operation.
Learn more →Explore production AI
Go deeper on architecture, evaluations, testing, security, observability, incidents, drift, and continuous AI operations.
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