The hardest part of building production research agents is not generating impressive answers. It is controlling the environment in which they reason. Systems that perform well on carefully selected examples can encounter more subtle problems in real expert workflows, including incomplete evidence, excessive context, incorrect source selection, weak evidence handoffs, lost constraints, and outputs that are difficult to verify.

In this session, you will explore what breaks when research and reasoning workflows move from demonstrations into production. Drawing on experience building AI systems for knowledge-intensive and regulated environments, you will examine context engineering, tool boundaries, orchestration, evaluation loops, observability, citation-backed synthesis, human review, latency, cost, and traceability. You will also explore why larger context windows do not remove the need for context discipline, and how to decide what context each step should see, what should be excluded, how evidence should be ranked, and when missing context should trigger reflection.

What You Will Learn

• Common failure modes that emerge when research agents move from prototypes into production workflows

• How context engineering, tool boundaries, evidence handling, and review processes influence system behaviour

• How to identify and strengthen weaknesses in production research and reasoning workflows

Who Should Attend

AI Engineers, Applied AI Practitioners, Platform Engineers, Staff and Principal Engineers, Software Architects building agent-based systems, Technical Leads responsible for AI system quality and trustworthiness, and teams developing research or knowledge-intensive workflows.