The team built a system that quantifies how much engineering work Devin delivers vs. what you pay, then backs that claim with up to $10 million per customer. They validated the methodology with independent data and benchmarks to prove Devin consistently delivers more output than its cost.

• Cognition is offering up to $10 million per customer as a refund/credit if Devin's engineering output doesn't exceed what the customer pays for it.
• The guarantee is backed by an "Engineering Velocity Score" tracking code changes, tests, deployments, and milestones, tied directly to invoices.
• Independent auditors get real-time read-only access to logs to verify data integrity and publish monthly benchmark comparisons against in-house teams, consultancies, and past Devin performance.
• Customers get a portal to track their own Velocity Score in real time, triggering automatic credits if performance falls 10% below the agreed threshold.

This article discusses the impact of coding agents on the roles within Engineering, Product, and Design (EPD) teams. With coding becoming easier, the focus has shifted from creating detailed product requirement documents to rapid prototyping and review, emphasizing the need for generalists and strong system thinking. It highlights the evolving nature of roles where builders and reviewers emerge as distinct categories.

• Coding agents shift the bottleneck from writing code to reviewing it—checking architecture, user fit, and design quality.
• Generalists who span product, design, and engineering become more valuable because they cut coordination delays and can use coding agents directly to validate ideas.
• Weak product thinking now carries a higher cost: bad prototypes flood the review pipeline and waste team effort.
• System thinking becomes a core skill requirement—engineers need architecture/API fluency, PMs need real user insight, designers need interface judgment—so agents can be guided well instead of producing more review work.

Engineers face difficulties in transitioning from deterministic programming to probabilistic agent engineering, as they often struggle to trust the adaptive capabilities of AI agents. Traditional practices, such as strict typing and error handling, clash with the need for flexibility and context-aware interactions in agent systems. Emphasizing the importance of semantic understanding and behavior evaluation, engineers are encouraged to embrace a new approach that balances trust and oversight.

• Senior engineers' deterministic mindset (strict typing, exhaustive error handling) actively conflicts with building probabilistic AI agents that need flexibility
• Trust in the system has to shift from verifying exact outputs to evaluating whether agent behavior is semantically reasonable given context
• Overly constraining agents with rigid rules to feel "in control" undermines the adaptive reasoning that makes agents useful in the first place