Token optimization

Aug 21, 2026

Agentic SDLC at Uber — Uday Kiran Medisetty & Adam Huda, Uber

Uber has transformed its software development with an agentic AI-powered factory, leading to a dramatic increase in engineer productivity. The presentation details six key infrastructure components: a unified model gateway with strict PII and safety guardrails, an MCP gateway for streamlined agent tool access and token optimization, agentified dev pods for rapid execution, a managed skills marketplace, a comprehensive context graph, and the Cortana AI assistant. Adam Huda then demonstrates an end-to-end feature development workflow, highlighting a critical shift to inner-loop validation (stopping short of CI) and automated, managed maintenance loops. The ultimate takeaway is that the bottleneck has moved from technical execution to strategic decision-making: "should we build it?" rather than "can we build it?"

Jul 14, 2026

Anthropic's Katelyn Lesse & Angela Jiang: Building an Ecosystem, not a Walled Garden

Katelyn Lesse and Angela Jiang, leaders of Anthropic's developer platform, outline their strategy built on a "three-layer cake": knowledge, execution, and coordination. They emphasize moving towards advanced "strategies" or meta-harnesses that assign distinct jobs to tokens, fostering a robust and open AI ecosystem. The discussion covers empowering builders, setting industry standards, and Anthropic's nuanced approach to an open platform versus a walled garden, focusing on architectural soundness over infrastructure ownership.

Jul 11, 2026

Stop AI Agent Hallucinations: 5 Techniques + Production Patterns - Elizabeth Fuentes, AWS

Explore five research-backed techniques to overcome common AI agent architectural limitations like hallucination, inefficient tool use, and rule violations. Learn how to implement semantic tool selection, Graph-RAG, multi-agent validation, neurosymbolic guardrails, and agent steering through code changes, not prompt engineering, and deploy them using AWS Bedrock Agent Core for robust and cost-effective AI agents.

Jun 28, 2026

We Cut 94% of AI Coding Tokens With a Local Code Index - Rajkumar Sakthivel, Tesco

Rajkumar Sakthivel details how an unexpected surge in AI coding tool costs led to the discovery that sending excessive, irrelevant context was the primary culprit. He introduces the Code Context Engine (CCE), a local retrieval layer that intelligently prunes context using AST-aware chunks, hybrid search, and relevance scoring, resulting in up to 94% token reduction and significant cost savings. The talk emphasizes that optimizing input context, not just the AI model, is paramount for efficient and accurate AI-assisted coding.