Moving Beyond Prompts to Reusable Agentic Workflows
Every tech team can use AI to speed up single tasks. Few can build an agentic system that actually runs complex, multi-step work without constant hand-holding, repeated mistakes, and lost context. Scaling AI beyond basic prompting isn't about using larger models—it's about structure, control and turning human expertise into reusable ways of working. The most effective systems preserve human judgment while giving agents the autonomy to drive long-horizon tasks to completion. In this hands-on one-hour session, we’ll dive into five core practices that tech teams need to master to build dependable AI workflows: Identify where simple planning breaks: Uncover the exact failure modes where heavy preprocessing and elaborate prompting reach their limits. Codify expertise into reusable skills: Capture operational procedures, domain judgment and validation rules that agents can execute across unfamiliar tasks. Keep long-horizon tasks coherent: Apply proven patterns for managing shared context, explicit decisions and inspectable state during complex handoffs. Balance agent autonomy with control: Layer automated checks, independent reviews and human-in-the-loop checkpoints to guarantee acceptable outcomes. Make every task improve the next: Turn real-world failures and feedback into vetted updates that continually sharpen your team's agentic workflows. This session is tailor-made for technical leaders who are ready to stop babysitting AI outputs and start scaling reliable systems. You’ll leave with practical tactics you can immediately apply to your own tech stack. Agenda: 9-9:10 a.m. - Registration & networking 9:10-10 a.m. - AI Session with Atif Khan 10-11 a.m. - Optional Q&A Speaker: As CTO at DataBraid, Atif Khan leads the development and application of agentic AI frameworks across software development, product lifecycle management and operations. Having delivered technical sessions for enterprise engineering teams and led the winning entry in a world-renowned data science an