Conversation with Mihail Eric on how agent-driven development is reshaping engineering work, faster iteration, new failure modes, and shifting team dynamics. Focus on validation, cost tradeoffs, and what breaks when code is mostly generated rather than written.

The blog argues that context graphs can serve as the system of record for reasoning, capturing how decisions are made, corrected, and carried forward across humans and AI agents. It shows that making context graphs real requires more than an abstraction: it demands a technical substrate, clear processes, and cultural norms that let organizations review, refine, and preserve judgment over time. When organizations pair emerging context‑graph technology with the cultural shift required to justify decisions, annotate reasoning, and protect shared context, they unlock a future where human and agent judgment reinforce each other, decisions become auditable, knowledge compounds, and the entire company grows more intelligent over time, which is the real trillion‑dollar opportunity.

Scaling LLMs in production requires balancing cost, latency, and performance. Through techniques like dynamic GPU scaling and TensorRT optimization, latency was reduced by up to 70%, while iterative learning and tight alignment with business goals ensured strong ROI.