Most enterprises have already automated the simple work. The next wave of value will come from complex, high-value workflows that span systems, policies, teams, approvals, and exceptions. But these are exactly the workflows where the existing agent-building options often break down. This session will show how enterprises can move beyond task automation and build agents that can safely execute more complex work with the right mix of autonomy, control, orchestration, and grounding.

The market is moving from isolated agent pilots to enterprise-wide agent programs. But as more teams build agents, CIOs and CTOs face a new challenge: agent sprawl, duplicated engineering effort, inconsistent behavior, rising risk, and limited visibility into what agents are doing. This session will show how enterprises can scale agentic AI without scaling complexity, risk, or operational burden by standardizing how agents are built, deployed, governed, and observed.

Getting agents into production is only the beginning. The bigger enterprise question is whether those agents are improving the metrics that matter: cost, resolution, productivity, compliance, customer experience, employee experience, and business impact. As AI adoption scales, enterprises need a way to continuously monitor, diagnose, and improve agent performance against real outcomes. This session will show how teams can turn agents from static deployments into continuously improving systems.

As agentic AI moves into production, enterprises are discovering that cost and performance cannot be managed separately. More reasoning, more model calls, more retries, and more orchestration can quickly drive up token spend, but cutting costs blindly can hurt accuracy, latency, compliance, and user experience. This session will show how enterprises can optimize the economics of AI agents without compromising the outcomes they were built to deliver. We’ll explore how the right platform can identify where costs are coming from, reduce unnecessary LLM usage, route work intelligently, use deterministic execution where it makes sense, and continuously tune agents for the best balance of quality, speed, risk, and cost.