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Related event timeline, sources and context from the news index.
At QCon AI, Susan Chang (Principal Data Scientist at Elastic) described how Elastic moved from siloed, ad-hoc agent evaluations to a unified production-grade evaluation framework that combines LLM-as-judge and deterministic rules, connects Python data-science evals with TypeScript production code, and uses deep tracing to detect regressions across RAG and cybersecurity workloads while preserving domain context.
At QCon AI, DoorDash engineers Swaroop Chitlur and Siddharth Kodwani presented the company’s journey building an internal GenAI platform for ~5,000 internal users, covering architectural bets, the shift from vendor-first to open-weights models, LLM and agent gateway design, and trade-offs between accuracy, latency, and cost. They emphasized API-/SDK-first design, focusing on product engineers as customers, embedding best practices, and prioritizing business impact over prototypical chatbots.
At QCon AI, Forter principal engineer Ben Maraney described a two‑week internal sprint that gave ~200 R&D staff hands‑on experience building AI agents. The team lowered friction by running a custom MCP server, combining no‑code and code platforms, avoiding complex RAG setups, and coordinating with security and legal to speed adoption.
Mallika Rao, former engineering leader at Twitter, Walmart, and Netflix, presents at QCon AI about the operational and system-design challenges of adaptive recommendation systems. She emphasizes real-time feedback loops, retrieval freshness, multi-stage orchestration, and end-to-end latency and cost trade-offs required to continuously learn and evolve recommenders in production.
At QCon AI, OpenAI engineer Vinoth Govindarajan presented 'The Agent Harness', arguing that many production agent failures stem from engineering and state-management issues rather than model hallucinations. Using case studies such as OpenClaw, he outlined core principles for reliable agent harnesses: explicit state ownership, serialized concurrent state mutations, scoped execution authority, and validating actions at the user-visible edge.
At QCon AI, LinkedIn engineer Ajay Prakash presented 'Contextual Agent Playbooks and Tools', an organizational context layer built on the Model Context Protocol (MCP) that supplies procedural memory, code search and runbooks directly to coding agents. LinkedIn says the system (used across hundreds of workflows) yields a 20% productivity boost with no loss in reliability and discussed its architecture and operational guardrails.
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