AI CPG Circle: From AI Experiments to Systems That Compound
As more teams adopt AI across workflows, operations, and decision-making, the challenge is no longer just generating outputs; it’s about building systems that can retain context, coordinate work, and improve over time, rather than restarting from scratch every session.
In this session, Berk Hiziroglu will walk through SUPERPOWERS and COMPOUND ENGINEERING, two open-source frameworks designed to help teams move from isolated prompting into scalable, compounding AI workflows.
Rather than focusing only on tooling, this session will explore a broader operational shift happening across AI-native teams:
How do you build systems that preserve organizational knowledge, coordinate multiple AI tasks reliably, and create workflows that actually improve with continued usage?
Using practical demos and real implementation examples, we’ll look at how orchestration, memory, and structured agent collaboration are becoming the next layer of applied AI systems.
We’ll cover:
• Moving from one-off prompting to repeatable AI workflows
• Why orchestration and memory matter in real-world AI operations
• How teams can structure AI processes that compound over time
• Multi-agent workflows: planning, delegation, review, and iteration
• Capturing learnings and operational knowledge across sessions
• Practical examples using Claude Code, SUPERPOWERS, and COMPOUND ENGINEERING
We’ll also explore:
• Where current agent workflows still break down
• The trade-offs between speed, coordination, and reliability
• How AI systems can support operational scalability beyond engineering teams
• What changes when AI becomes part of ongoing organizational processes, not isolated experiments
This session is designed for builders thinking beyond demos - toward AI systems that can evolve, retain context, and support real operational workflows over time.
Speaker: Berk Hiziroglu
Berk works at the intersection of AI systems, agent workflows, and applied automation. His work focuses on designing practical frameworks that help teams orchestrate AI agents more reliably in production environments, with a particular interest in workflows that improve through iteration rather than resetting every session.