AI & Agentic Workflows
Most AI demos look great and fail quietly in production. The reason is rarely the model, it's arithmetic: an agent is a chain of steps, and reliability multiplies. This walks through the two questions a real evaluation answers: why agents fail more than their parts suggest, and how to decide when one is safe to ship. Runs entirely in your browser on illustrative data.
Gartner predicts over 40% of agentic-AI projects will be canceled by the end of 2027, citing cost overruns, unclear value, and inadequate risk controls.
An agent that handles a refund touches a dozen steps: read the ticket, pull the order, check the policy, draft the reply, file the credit… If each step succeeds 95% of the time, twelve steps succeed 0.9512 ≈ 54% of the time, a coin flip with extra steps. The fix isn't a smarter model, it's the harness: checkpoints that verify the work so far and retry a failed segment before the damage spreads.
Zoom into one link in the chain: a support agent drafting replies to customers. When do you let it send without a human? The agent's stated confidence isn't its real accuracy, and that gap is exactly what an eval measures. Tune the guardrails and watch the ship decision respond.
Set the guardrails
Below the bar, the answer is escalated to a human instead of sent automatically. Raise it and the agent sends less, but what it does send is right more often.
A second step that checks the draft against the source before it goes out, the single biggest lever on hallucinations. Costs a little more per answer.
Ship decision
Illustrative simulation, not a benchmark of any specific model. A real engagement evaluates your agent on your tasks, your data, and your risk tolerance.