As Sankar Narayanan 'SN' underscores, #EnterpriseAI starts to look very different once agents move beyond simple chat interactions and begin operating within real workflows. They need the right instructions, the right data, the right state, the right tools, and the right controls at each step. Our latest blog with OpenAI explores this through a practical five-layer architecture for context engineering and why continuous evaluation is essential to making agents reliable in production. Read the blog below: https://lnkd.in/gWmfn5Pp
#CIOs are losing sleep. They're losing sleep over a question they can't answer yet: "If an AI agent makes a wrong call, or surfaces data it shouldn't, can we trace exactly why?" For most #enterpriseAI deployments today, the honest answer is NO. Teams spend months fine-tuning prompts, swapping models, chasing benchmarks. Only to find their agents still hallucinating in production, surfacing data they shouldn't, or making decisions no one can trace. And the reason is almost never the model. It's everything the model can't see, shouldn't see, or has already forgotten. It is about how context is managed. OpenAI and Fractal just published a deep-dive on Context Engineering. Think of it as the engineering layer between a capable model and a production-ready system. One that is capable of dealing with real operational workflows that span hundreds of turns and dozens of API calls and must meet strict compliance standards. Not prompt tweaking. Not model swapping. But a disciplined, layered approach to governing what information flows into an AI agent: when, why, and under what constraints. We've distilled what this looks like in practice into a Five-Layer Context Architecture: → Identity - who the agent is and what it's allowed to do → Knowledge - grounded, compliant retrieval → Dynamic state - live permissions and entitlements → Memory - what to keep, compact, or prune → Tools - least privilege access and clean outputs Each layer is an explicit engineering boundary. Testable. Governable. Improvable. An emerging common trait amongst teams succeeding at enterprise AI initiatives is that they don’t treat context as a prompt problem and instead treat it as infrastructure. That shift, from "let's write better prompts" to "let's govern what the agent knows" likely separates a six-month pilot from a system the business actually relies on. Grounding agent design on OpenAI-native patterns and pairing it with Eval-Driven Development can lead to deliberate progress towards agents that are explainable, auditable, and production-ready. Not just impressive in a demo. Full blog in the comments. Authors: Shikhar Kwatra, Soumo Chakraborty, Sakshi Ray, Amey Gujre, Suvam Ray Question: what's the context failure you've seen most often in enterprise AI? Is it wrong information, stale information, or information the agent simply never had? Satish A. Raman, Himanshu Nautiyal
