Moving an agentic workflow out of the pilot stage is where many enterprise AI programs stall.
A workflow can look impressive in a demo and still fall short in production, and prompt-level testing alone rarely tells you whether a workflow is truly ready.
Agents that answer customer queries, flag suspicious transactions or process claims need to be safe, evidence-based, policy-compliant and appropriately constrained.
Join us and Innodata for a practical, conversational look at how to validate AI and agentic workflows before deployment, and how to keep them reliable once they’re live & begin to scale.
Why attend?
- Production readiness is harder to define than it looks. Many teams test outputs but miss the failure modes that matter most to the business.
- The same principles apply across very different workflows. From customer support and AML in financial services to claims processing in insurance, see how validation requirements shift with each workflow and its business objective.
- There’s a clear path from pilot to production. Follow a framework that takes you from experimentation to validation, deployment and continuous monitoring.
What you’ll learn
- What you actually need to validate before an AI workflow is production-ready
- How to connect technical evaluation metrics to the business KPIs your leadership cares about
- Where automation is sufficient and where human judgment is still required
- How to use the same framework to monitor reliability long after deployment
Speaker
Jyotsna Jha Vice President of Product, AI/LLM Practice, Innodata
Jyotsna leads product strategy and execution for Innodata’s agentic evaluation, observability and feedback systems – helping enterprises build, govern and continuously improve AI agents operating in real-world workflows. She has built and scaled AI products across real-time intelligence, intelligent document processing and data platforms.
