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AI coding agents: measuring ROI before you scale them

Gartner's 2026 market guide for enterprise AI coding agents reports that 90% of engineering leaders see improvements from the tools, against a net average productivity gain of 19.3%. A separate Gartner analysis of technology adoption puts the share of software engineering leaders reporting significant ROI from AI across the development lifecycle at 35%. The distance between those figures is not a tooling failure. It is what happens when a team adopts agents without a pre-adoption baseline, budgets for them as seats while vendors move to usage-based pricing and expects acceleration in the parts of delivery that agents do not touch.
Tudor Iordache - 16 Sep 2026
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  • Why a product design phase comes before the build budget
    Alex Marciuc - 30 Jul 2026

    Why a product design phase comes before the build budget

    A product design phase is four to six weeks of structured work sitting between an approved idea and a committed build budget. It produces a validated prototype, a prioritised feature breakdown with ranged estimates, a technical architecture with integration risk resolved, and a release plan with cost scenarios attached. The output is not a set of design artifacts. It is a narrower cone of uncertainty, and a scope that can be cut without breaking. Teams that skip the phase rarely save the money. They move it into the build, where it gets spent on rework nobody planned for.
    Featured Startup Product design
  • Six principles for AI systems that work in production
    Tudor Iordache - 2 Jul 2026

    Six principles for AI systems that work in production

    The AI projects that work in production share a small number of structural decisions. None of them is about the model. All of them are about the system around the model: the threshold defined before the build begins, the constraint treated as architecture rather than an obstacle, the surface designed as the actual product, the MVP scoped as the first instance of the platform, the human kept in the loop as a permanent feature rather than a transitional one. The discipline of building AI well is the discipline of getting these decisions right early enough for the model to do its job, and of refusing to defer them until the cost of deferral becomes unavoidable.
    Technology Featured AI
  • Five MVP decisions that turn an AI MVP into an enterprise AI platform
    Stefan Sarbu - 29 Jun 2026

    Five MVP decisions that turn an AI MVP into an enterprise AI platform

    The MVP-to-platform transition fails not because the technical architecture is wrong, but because most teams treat it as a scaling problem when it is actually a redesign problem. The MVP gets you to one customer. The platform requires you to be honest about what was specific to that customer and what is genuinely transferable. The teams that get this right do not generalize reactively. They build the MVP knowing which decisions will need to be revisited at the platform stage, and they revisit them deliberately.
    Technology Featured AI Architecture
  • Human-in-the-loop AI systems where judgement stays in the workflow
    Tudor Iordache - 24 Jun 2026

    Human-in-the-loop AI systems where judgement stays in the workflow

    Human-in-the-loop is not the version of AI you build while waiting for the model to get good enough to replace the human. It is the version of AI that actually works in domains where being wrong is expensive. The teams treating it as a transitional design are building systems that will eventually be replaced. The teams treating it as the destination are building systems that compound trust and improve over time.
    Featured Publishing AI
  • How do you build RAG systems that work in production?
    Stefan Sarbu - 18 Jun 2026

    How do you build RAG systems that work in production?

    Most RAG implementations are built to produce answers. The ones that work in high-stakes environments are built to produce answers with calibrated confidence, source attribution the user can verify, and behavior that changes when confidence is low. The hard engineering work sits in four places most teams underestimate: chunking strategy, embedding quality, confidence calibration, and how the system surfaces its own uncertainty. The teams that get RAG into production treat confidence as a product feature rather than a model property, and they treat "I do not know" as a legitimate answer the system needs to be trusted to give.
    Trending Featured AI
  • AI browser agents: the gap between demo and production
    Ilie Ghiciuc - 10 Jun 2026

    AI browser agents: the gap between demo and production

    Browser-use AI agents are most valuable when they are a component in a larger system, not when they are deployed as a complete solution. The teams getting durable value treat the agent as a navigation layer feeding into deterministic downstream processes, with serious operational infrastructure around it. The teams that struggle deploy the agent and expect the rest of the system to follow.
    Technology Trending Featured AI
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