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Stay updated with our latest research findings, product developments, and insights into AI optimization
Stay updated with our latest research findings, product developments, and insights into AI optimization
From brittle demos to reliable production agents: How to close the demo-to-production gap and ship agents that actually work.
There is a gap between brittle agent/workflow demos and fully working reliable AI applications. Closing that gap is where most teams burn months: debugging failures, tuning prompts, swapping tools, and still shipping something brittle.
What if someone told you 90% accuracy per step is "good enough"? It isn't. Even a 5-step workflow only succeeds ~59% of the time (0.9^5=0.59). And at 30 steps it's ~4% (0.9^30=0.04). That neat recorded agent demo wouldn't reliably work when you need it to. As Karpathy puts it: demos are "works.any()", products are "works.all()".
The latest SWE-bench Pro results from Scale AI reveal the extent of the problem. While frontier models score over 70% on simplified "Verified" tasks (the clean demos), their performance crashes to just ~23% when faced with messy, real-world codebases. When you move into private, commercial environments, that number can drop as low as 14.9%.
As Anthropic noted in their blog on 9 Jan 2026, "Agents use tools across many turns, modifying state in the environment and adapting as they go—which means mistakes can propagate and compound", "…capabilities that make AI agents useful—autonomy, intelligence, and flexibility—also make them harder to evaluate."
The search space across these choices is combinatorially large, and each knob impacts accuracy, latency, and cost. Teams looking to build production-grade agents and workflows need to rigorously evaluate and optimize each tool/component in addition to optimizing the workflow/agent end-to-end to get the best performance. As OpenAI mentioned in their recent blog, the process starts with specifying or defining what "great" means, measuring when and how the system fails or succeeds, and finally improving and learning from errors, i.e. repeating the specify —> measure —> improve loop.
Skipping these steps, as Anthropic points out, doesn't work either: "…once an agent is in production… building without evals starts to break down. The breaking point often comes when users report the agent feels worse after changes, and the team is 'flying blind' with no way to verify except to guess and check. Absent evals, debugging is reactive: wait for complaints, reproduce manually, fix the bug, and hope nothing else regressed."
Andrew Ng provides a neat recipe for how to build reliable agents in his recent course on deeplearning.ai
A recent paper on Measuring Agents in Production from Stanford and UC Berkeley makes the same point: "Reliability remains the top development challenge, driven by difficulties in ensuring and evaluating agent correctness."
And as Anthropic mentions, evals are a competitive advantage: "When more powerful models come out, teams without evals face weeks of testing while competitors with evals can quickly determine the model's strengths, tune their prompts, and upgrade in days."
And as Anthropic notes, its better to start the evaluation and optimization journey sooner rather than later: "Teams that invest early find… development accelerates as failures become test cases, test cases prevent regressions, and metrics replace guesswork. The value compounds, but only if you treat evals as a core component, not an afterthought."
So what are you waiting for? We're offering a free tier with core eval + optimization features for agents. Try it here and get your first optimized agent in minutes.
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