AI coding agents can finish a task before you’ve finished framing it, but that speed hides a harder problem: how much of the work can you trust, verify, or even understand? This discussion looks past the demo magic and into the practical bottlenecks teams are hitting as agents move from autocomplete to semi-autonomous collaborators.

• Validation is the real constraint. Agents can generate code fast, but tests, checks, and review harnesses still decide what is safe to ship.
• Team structure is starting to shift. Product, engineering, and design roles are bleeding into each other as more people can inspect code, propose changes, and unblock themselves.
• The skill gap is changing shape. Clear articulation, planning, and delegation matter more when engineers are effectively managing agents instead of writing every step by hand.

The hard part is no longer getting code written but knowing what to trust, what to verify, and where humans still need to hold the line.

Cut latency 70% or burn cash on idle GPUs - running LLMs in production is a constant trade-off. This breakdown shows what it takes to move from demos to real systems, where cost, throughput, and architecture decisions matter more than model choice.

• Cost isn’t fixed - it’s shaped by architecture. Bigger GPUs can be cheaper overall if higher throughput reduces total runtime.
• Cold starts and scaling are the hidden bottlenecks. Preloading models, faster storage, and scheduled or dynamic scaling cut minutes off spin-up times.
• Optimization compounds. Techniques like TensorRT, batching, and KV cache usage unlock major gains without changing models.

The real advantage comes from tuning the system around your workload, not chasing the next model release.

If context graphs are meant to become the memory layer for agents and organizations, the hard part is not drawing nodes and edges. It is capturing why a decision happened, who approved it, what constraints shaped it, and whether it later proved right. This piece argues that context graphs only become useful when they can survive real company messiness like review workflows, legal sensitivity, local jargon, and scale.

• Decision traces need governance. If humans do not review, correct, and approve them, the graph risks becoming a polished record of bad reasoning.
• Reasons need dual encoding. Short natural-language explanations plus structured tags give humans something readable and agents something stable to reason over.
• The data layer has to handle reality. Time-aware context, multimodal artifacts, integrations, retention rules, and fast writes are all part of making this work outside a demo.

The real blocker is not the graph itself but whether an organization can turn judgment into something structured, reviewable, and worth trusting later.

• Amsterdam - April 21
• Boston - April 27
• San Francisco - May 15