One question I often hear from prospective users of our technology is where the value will come from. It won’t come from larger models. Instead, it will originate from applications that are consistently and clearly useful to the people and workflows they support. “All models are wrong, but some are useful” is even more applicable to agentic systems: if an autonomous workflow doesn’t reliably improve a business metric, it’s just a demo.
Redefine success as usefulness
For years, discussions about AI have focused on model capability, scaling laws, and speculative timelines for AGI. However, in real businesses, the more important question is simpler: does this agent reliably help people complete important tasks faster, better, or more safely? Usefulness means connecting every agent and workflow to a specific outcome, such as shorter cycle times, fewer manual reviews, higher throughput, or better compliance, and designing around those results instead of just focusing on a model’s latest benchmark score.
Design around real workflows, not chat
Agentic AI becomes effective when it integrates seamlessly with how organizations actually function. This involves starting with existing processes, systems, and pain points, then embedding agents into those flows instead of simply adding a chat interface. Useful agents read from and write to core systems, respect roles and approvals, and produce outputs in formats already used by downstream teams. This way, “AI in the loop” feels like an enhancement of work, not extra effort.
Constrain, ground, and iterate
Developers often get stuck debating hallucinations, data quality, and integration challenges in the abstract rather than using them as design constraints. The most effective agentic applications take a different approach: they clearly define what the agent can do, base it on curated data and retrieval, and implement guardrails plus human checkpoints where the stakes are high. Over time, they gradually expand scope based on observed performance, feedback, and trust, making the agent immediately useful on day one and improving it gradually with each release.
Exploit today’s LLMs, don’t wait for tomorrow’s
Frontier models already understand complex instructions, reason across extended contexts, and coordinate tools in ways that can greatly increase productivity. However, most organizations have only begun to explore their potential. Instead of waiting for a new architectural breakthrough, AI developers can use the current generation of models as powerful engines supporting well-designed workflows, memory, and orchestration. The real frontier is no longer just intelligence; it’s usefulness at scale.
