{"slug": "are-code-reviews-still-relevant", "title": "Are code reviews still relevant?", "summary": "Ansight is proposing \"Evidence Engineering,\" a data-centric method for validating software produced by agentic coding tools such as Claude, Codex and Cursor, arguing that manual code review is becoming a bottleneck as code volume grows. The approach replaces line-by-line review with captured evidence in three forms — ex vivo replay (e.g., PostHog or Sentry replays and app logs), in vitro external observation (screen recordings, network activity, telemetry, OS behaviour) and in vivo runtime interrogation via an in-process bridge such as an MCP tool exposing live app state. Ansight argues that if evidence shows no memory leaks, stable framerates, correct user journeys and correct network, file I/O and app state, the implementation is fit for purpose regardless of how the code was written.", "body_md": "As the volume of code and product we produce increases, it’s becoming a Sisyphean effort to manually review and test everything. It’s fair to say, as an industry, we are experiencing a real-time breakdown in the established software development lifecycle methods of the past 50 years.\n\nBe honest, how much of the code produced by your last Claude, Codex or Cursor session did you actually read and review?\n\nDid you do a few test runs, ask Claude to summarise the key code changes and then go “looks good to me”?\n\nHow many PRs is your company needing to review a week? Is code review now your bottleneck?\n\nWhile the underlying principles of software engineering should and must still remain (SOLID, Clean Code, DRY etc), this volume of product/code means it may not be possible to fully apply “best practices” and remain competitive in the age of agentic software development.\n\nTherefore, rather than focusing on code quality per se, we could instead focus on measurable outputs that point to the *evidence* of the code’s correctness.\n\nFor example:\n\n- Does the video produced by an agentic test match our expected outcomes for that user journey?\n- Over a 30-minute app session, did the memory usage remain stable?\n- When the feed list scrolls, did the FPS drop substantially?\n- While fetching a series of photos, did the app’s memory spike significantly?\n\nAt Ansight, we call this philosophy **Evidence Engineering**; a data-centric method to software proofing.\n\nAt the core of this is a question, and dare I say a mildly controversial one:\n\n**Does it truly matter how the code is implemented, as long as the produced *evidence* supports that the software works as expected?**\n\nIf all evidence supports that the software works correctly — there are no memory leaks, framerates are stable, user journeys are correct, network and file I/O is correct, and app state is correct — then the implementation is fit for purpose.\n\n## How does this apply in practice?\n\nAt Ansight, we view evidence coming in three forms:\n\n- **Ex vivo — replay:** Evidence after the fact; revisit a captured execution and inspect its recorded evidence. Replay does not require deterministic re-execution. A PostHog or Sentry replay, or app logs, would be ex vivo evidence.\n- **In vitro — external observation:** observe a live app through screens, inputs, logs, network activity, telemetry, and OS behaviour. Capturing a screen recording or externally profiling the app is an example of in vitro evidence.\n- **In vivo — runtime interrogation:** question the live app through an in-process bridge to expose its internal state. An MCP tool exposed by the running app to describe its current state (e.g., a 3D scene graph or in-memory object state) is an in vivo evidence example.\n\nTogether, these three evidence types **must** be captured and structured into a clean, agent-native surface. An agent must be able to slice, dice, inspect and query discrete chunks of all data channels to more easily infer, problem solve and arrive at the **correct** solution.\n\nTherefore, in evidence engineering, all development and test sessions must produce an extremely detailed record to allow an agent (or human) to validate what happened and then meaningfully investigate issues from data.\n\nSome examples:\n\n- Touch locations, screenshots and visual trees: reconstruct the user journey and the UI state throughout it.\n- Framerate, CPU pressure, memory usage: identify areas of poor performance, and help correlate them with the behaviour that caused them.\n- App logs, navigation transitions: the app’s behaviour.\n- Network traffic, database queries/state, file system snapshots: the app’s inputs/outputs.\n- App state snapshots: In-app MCP/remote tools that can be dynamically queried to snapshot an exact moment in time. These snapshots must be stored into a replayable timeline.\n- Annotations: point-in-time, or visual, annotations made by either a human or another agent that can be used to specify points of interest.\n\nEvidence engineering is an agent-native software development methodology, underpinned by the philosophy that agents are only limited by the data they have access to.\n\nEstablishing a strong evidence base to **prove through data** that code works as expected enables us to confidently take a few steps back from the implementation details, take a breath, and lessen our overall cognitive load.\n\n## How Ansight Applies Evidence Engineering\n\n[Ansight](https://www.ansight.ai/docs/local-player/session-capture) brings all three lenses together for mobile applications: recorded sessions for replay, live capture for observation, and [runtime tools](https://www.ansight.ai/docs/local-player/live-tools) for interrogation.\n\nOur free CLI tool is able to capture all of these data points (and any arbitrary custom app state), and gives agents an extremely rich tool suite to inspect this data.\n\nWe support all major mobile frameworks and have been successfully applying this engineering philosophy at [Red-Point](https://www.red-point3d.com) (our 3D climbing guide for rock climbers) for the past 18 months.\n\nTry out our free CLI and reach out to us if you’re interested in talking more about this philosophy.", "url": "https://wpnews.pro/news/are-code-reviews-still-relevant", "canonical_source": "https://www.ansight.ai/articles/evidence-engineering", "published_at": "2026-10-27 00:00:00+00:00", "updated_at": "2026-10-27 01:43:34.920574+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "agent-protocols", "developer-tools"], "entities": ["Ansight", "Claude", "Codex", "Cursor", "PostHog", "Sentry", "MCP"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/are-code-reviews-still-relevant", "markdown": "https://wpnews.pro/news/are-code-reviews-still-relevant.md", "text": "https://wpnews.pro/news/are-code-reviews-still-relevant.txt", "jsonld": "https://wpnews.pro/news/are-code-reviews-still-relevant.jsonld"}}