Brandon Gubitosa
June 04, 2026
11 min read
June 04, 2026
11 min read
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AI agent explainability is what determines whether an AI Agent gets deployed to solve real-world problems, or remains a sidekick on non-critical enterprise tasks. It is about understanding an agent's reasoning behind decisions, the alternatives it considered, and how confident it was.
Most engineering teams shipping AI-authored code are trusting a black box right now. AI coding agents open more PRs than human reviewers can meaningfully read. The agent generates the diff, a reviewer or agent merges the code, and nothing in between captures which rules the agent checked, what it skipped, or why the change was safe to ship. The team ends up owning production code it can't explain.
Making AI-authored code explainable is now both an engineering problem and a compliance problem. Below, we cover why explainability is structurally easier on the verification side of an AI workflow than the generation side, the five questions any AI agent in your agentic software development lifecycle (SDLC) should be able to answer, what explainability does to delivery metrics, and what to look for in tooling that has to stand up to a SOC 2 audit or an EU AI Act review.
The gap between what an agent logs and what a human can actually understand is what ultimately keeps agentic products from getting traction.
Explainability has three primary jobs:
The primary consumer here is not the user. It is their manager, their compliance team, their customer, or their future self six months later trying to understand a past decision.
When it comes to verifying an agent's output there’s a huge gap between agents that verify code produced by AI agents and those that generate the code.
Generative agents write plausible code by predicting likely next tokens, so the same prompt can produce different code each time. Verification agents do something different. They judge a finished piece of code against a known rule or policy that lives in your codebase.
A model's "temperature" setting controls how much randomness it adds. Above zero, the same input can return different outputs on different runs, which makes those results hard to reproduce later, when an auditor asks you to. Research on compiled AI systems cites evidence that outputs can still differ run to run, even at a temperature of zero. So a generative agent leaves no stable record to audit. You can't re-run it and reliably get the same answer back.
Compare that to a verification agent. Give it a fixed input and a fixed rule, and you get a traceable decision, such as rule X matched pattern Y at line Z. Anyone on the team can reconstruct that decision from the rule and the changed code. The verification agent's reasoning is the rule match itself, a concrete record that exists outside the model. The generative agent's reasoning is whatever happened inside the model, which you can only guess at after the fact and never reproduce exactly.
The VeriGuard framework splits the same two jobs. One agent generates, a second agent checks the first one's output, and when the check fails, the second agent points to the specific thing that's wrong rather than just saying no.
That same split shows up in production. Abnormal AI, an AI-native cybersecurity company, runs CodeRabbit as a single enforcement layer over both its AI-generated and its hand-written code. The team accepts more than 65% of CodeRabbit's critical-severity comments. This works because AI usually fails by getting your specific codebase wrong, not by getting syntax wrong. So the tool that checks code against your codebase's rules is the one positioned to explain why it flagged something.
Any AI agent in the agentic SDLC should be able to answer these five questions, with a real answer tied to the diff:
What did you change and why? The agent should produce a structured walkthrough of the changes it evaluated, grounded in the PR's actual content. A summary that parrots the PR title is not an answer.
What did you check? The agent should identify which rules fired and which policies it consulted, including where those policies live in version control. If the answer is "I used my training data," the agent has no explainability.
What did you not check? This is the hardest question and the most important one. An honest agent tells you where its coverage stops. Did it validate against the linked Jira ticket? If the answer is silence, you're trusting a black box.
What's your confidence, and how is that number calibrated? A confidence score lets an auditor check later that your "escalate to a human" rules fire off when they should have. But a raw score on its own means little, because a model can be confidently wrong. The agent should say what the number is measured against.
What would change your recommendation? A useful review comment explains what condition would flip the judgment. "This is safe assuming no concurrent writers" is explainable. "LGTM" is not.
If the agent can't answer these in plain language tied to the diff, it isn't explainable.
Explainability can make a single review take longer while improving end-to-end delivery. The 2025 DORA report (~5,000 respondents) names the tension. AI adoption correlates with higher delivery throughput and, at the same time, with worse delivery stability. Thirty percent of respondents reported little or no trust in AI-generated output.
When a reviewer doesn't trust what the AI produced, they're left with two bad options. They re-check everything by hand, which cancels out the time the AI was supposed to save. Or they wave it through without really understanding it, which lets more bugs reach production. Neither is good. The way out is an agent that shows its work, so the reviewer can trust specific findings instead of trusting the tool wholesale.
CodeRabbit's State of AI report found that PRs co-written with AI produce 1.7x more issues per PR than human-only PRs. On the messiest 10% of PRs, the gap widens to 2.11x. An agent that tells you what it checked and what it skipped lets a team match its trust to what the agent actually covered.
Common App, which handles application data full of personal information, puts a verification agent in front of its reviewers. It cut code review time by 35% and dropped from two human reviewers per PR to one, with CodeRabbit handling the routine checks.
A record of what the agent checked also helps after something breaks. When a finding names the rule the agent applied and points to the exact lines, the team fixes the bug faster. They jump straight to the cited rule and lines instead of hunting for the problem all over again. The time to fix a problem drops most when the PR is already packed with issues. And over time, seeing specific, fixable findings builds reviewer trust, because an engineer can check the reasoning directly rather than take it on faith.
For engineering leaders, the explainability that matters lives in the operational layer, the logs and records an auditor cites in a SOC 2 audit or a post-incident review. Most writing on the topic stops at the model and never gets there.
For AI systems, a SOC 2 audit may ask for AI-specific evidence: logs of which model version ran, records of each high-stakes decision it made, and monitoring that flags when the model's behavior drifts over time.
Under change management, the auditor's question is blunt. Can you prove who reviewed a given change, when they reviewed it, and that it met your security policies before it merged? Code that an agent writes and runs on its own, with no human sign-off, fails that question today.
The EU AI Act pushes in the same direction. For high-risk systems, it calls for logging, written technical documentation, and the ability to trace how a system behaved after it went live. Under Article 113, high-risk obligations for Annex III systems were set to apply from 2 August 2026. A late-2025 amendment package, the Digital Omnibus, was provisionally agreed in 2026 and is expected to defer that Annex III timeline. The engineering case and the compliance rules point the same way. Both want a logged, inspectable record of who decided what, and on what basis.
Access controls and tamper-proof logs close the gaps regulators test for. They keep records long enough and prove who did what. This is the layer a VP has to defend when an auditor asks who approved a given merge and on what basis. Our enterprise tier ships that layer. It includes SOC 2 Type II certification, the option to run it on your own servers, a guarantee that your code isn't retained, single sign-on, role-based access controls, and exportable audit logs. Together those give you the documented trail of human accountability and approval that SOC 2 expects.
When you model the real cost of building internally, not just the initial build sprint but the maintenance team, model evaluation cycles, infrastructure, security reviews, and internal support, the numbers look very different from the back-of-envelope calculation that usually kicks off the project.
Cloudflare's engineering blog is a rare first-person account of building AI review tooling in-house at real scale. The hard parts the Cloudflare team hit included wiring plugins together and keeping the CI/CD pipeline reliable. On top of that, the team has to calibrate how much to trust the AI. EY's hallucination-risk guidance, published in January 2026, lays out a step-by-step plan for production use, with concrete targets for answer quality and for citing sources.
Writer, an enterprise AI platform, looked at building its own code review and bought instead. It reports saved time for its team leads and a single style guide enforced across the whole team.
CodeRabbit's own verification setup took years to build. Our documentation describes layered code analysis that runs more than 50 separate tools, including static analyzers and security scanners (SAST, which scans source code for vulnerabilities), plus model orchestration and a memory that learns from past feedback. All of that gets better the more the platform is used. Rebuilding the equivalent in-house means recreating that whole operational layer and keeping it running at scale.
Good SDLC tooling leaves a clear record for every decision: which rule it applied, where that rule came from, and exactly which lines of code it touched. Tooling that can't produce that record fails the bar auditors and engineers already hold it to, especially in the Agentic SDLC era.
CodeRabbit's verification architecture, including Codegraph analysis and audit logging, is one example that delivers these records as part of the platform. But the standard holds regardless of which tool you pick.
The compliance deadlines are real, the engineering metrics are measurable, and build costs are systematically underestimated. So the test to carry into any evaluation is simple: ask the agent the five questions, and keep the tool that answers in plain language tied to the diff. Every line still earns its merge.
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