The excitement around AI agents has moved quickly from demos to deployment, but real-world implementation has revealed a more complex reality. While some agent-based systems are delivering measurable gains in productivity, customer operations, and enterprise automation, others struggle with reliability, cost overruns, workflow failures, and governance concerns. This session brings together practitioners who have moved beyond experimentation to share candid lessons from the field. Through practical examples and implementation experiences, the discussion will separate hype from reality and explore what it truly takes to make agents work consistently at scale.
Key Discussion Points: - Real-world agent deployments and the business outcomes they are delivering
- Common failure points, including hallucinations, workflow breakdowns, and tool misuse
- Why proof-of-concepts succeed while production implementations often struggle
- The growing importance of evaluation, testing, and agent observability
- Lessons from emerging multi-agent and agent-to-agent collaboration models
- Managing reliability, latency, escalation paths, and human intervention
- Hidden costs of autonomous systems, including monitoring and operational complexity
- Which enterprise use cases are proving most successful today and which remain premature
- The rise of agent engineering as a distinct discipline within AI teams
- Practical frameworks for moving from impressive demos to dependable business systems
