QCon AI Boston 2026: Production AI Moves Beyond Prompts to Platforms, Harnesses, and Evals
QCon AI Boston 2026 focused on the operational challenges of deploying AI agents: improving context management, securing agents with a "harness," and adopting a comprehensive engineering model for delivering AI.
Theme: from "prompt-crafting" to "production-ready"
QCon AI Boston 2026 (reported by Tatiana Fesenko) put the spotlight on the operational challenges of deploying AI agents, stressing that production AI needs robust infrastructure, not prompt tricks. The key themes it distilled:
- Improving context management: the core bottleneck for long-horizon agents — how to keep useful information and drop noise within a limited window;
- Securing agents with a "harness": wrapping a layer of constraints and validation around the agent, controlling what it can call and change so behavior stays governable and auditable;
- Adopting a comprehensive engineering model: treating AI delivery as software engineering — evals, observability, regression — rather than tuning it once and shipping.
A clear industry signal
Those three words — Platforms, Harnesses, Evals — sketch the maturity path from demo to production: prompts decide whether it runs once; platforms, harnesses, and evals decide whether it runs reliably ten thousand times. For teams pushing agents into production, it's a pragmatic checklist: does your context management have a policy? Does the agent have a harness as backstop? Do outcomes have regressible evals?