Jeff Crume examines Jev, a new AI approach that makes structured decisions rather than generating text, through a cybersecurity lens. He argues that confidence scores are not evidence, that AI decisions expand the attack surface, and that observability, decomposed reasoning, independent controls, and human oversight are needed before trusting them.
Evan Kormos of Coinbase presents a practical framework for bringing AI agents into policy-driven, regulated workflows. He models processes as DAGs with loops inside each node, argues that a loop is only as trustworthy as its check, and shows how evals and traces create a new confidence mechanism. Agents then move through supervised, sampled, and exception-only stages.
Martin Keen explains loop engineering as the next step beyond prompt engineering: designing systems where AI agents act, observe, adjust, and repeat toward verifiable goals. The discussion covers goal design, triggers, context and persistent state, tools via MCP and APIs, verifier subagents, and why humans must remain in the loop to manage comprehension debt, intent debt, and cognitive surrender.
Harish Gaggar of Intuit Credit Karma walks through an internal paved path for taking enterprise AI agents from experiments to production. The talk covers portal-based registration and provisioning, a centralized authenticated MCP gateway with custom reviewed MCP servers, gateway monitoring with OpenTelemetry, Tempo, Prometheus, and Grafana, LangGraph workflows with policy checks and human approval, durable memory through threads and checkpoints, agent tracking and evaluation records, PII filtering, and context optimization for token reduction.
Michele Grant argues that an AI agent's technical accuracy is not enough to justify running a core business process. Using an illustrative M&A integration scenario, she lays out a framework built on measurable value against a baseline, three lines of accountability, a Human Works Standard, auditable evidence such as decision logs and model inventories, and staged autonomy governed by independent sampling and an autonomy scorecard.
Viren Baraiya, CTO of Orkes and original creator of Netflix Conductor, explains why production agents behave like applications built from microservices. He argues for splitting the work between a non-deterministic LLM that plans and a deterministic, durable harness that executes, with approval gates, idempotent steps, and recorded side effects. He frames agent harnesses as late-bound sagas and demos an SRE remediation agent whose plan is compiled into a Conductor workflow.