As software becomes increasingly agentic, technical due diligence readiness has evolved from an event into an operating capability. As a result, investors, PE operating partners, boards, and corporate development teams are asking a different class of questions. They’re no longer just evaluating whether your architecture scales or your codebase is maintainable. Instead, they want to understand how autonomous systems make decisions, who owns them, what they cost, and how they’re governed. Those are fundamentally different questions, and most engineering organizations, even very good ones, aren’t yet prepared to answer them.

Technical due diligence has always been about reducing uncertainty. In the past, that uncertainty mostly lived inside the technology itself: could the platform scale, was the architecture sound, how much technical debt existed. Those questions still matter. But in the agentic era they’re no longer sufficient, because software increasingly depends on external models, autonomous workflows, AI-generated code, and providers that evolve independently of your release cycle. So the engineering challenge is no longer just building reliable systems. It’s building organizations that can explain, govern, and adapt those systems over time. The technology changed. The bigger change is organizational.

“Show Me Your Agent Logs”

No diligence team actually opens a review by literally asking for agent logs. The phrase is shorthand for something bigger: can you explain what your autonomous systems are doing? Can you trace an important decision back to a responsible human? Do you know every production agent, what it’s authorized to do, and where its authority ends? And can you demonstrate governance rather than simply describe it? Those are increasingly the questions underneath every technical due diligence readiness review, regardless of whether the company is two years old or twenty.

The organizations that answer them well aren’t necessarily the ones using the newest models or shipping the most AI features. Instead, they’re the organizations that have made their engineering systems, and their decision-making, legible. Technology rarely fails diligence because it’s imperfect. It fails because nobody outside the engineering team can understand it.

The Biggest Change Isn’t AI. It’s Continuous Diligence.

Technical due diligence used to happen at predictable moments: before a funding round, during an acquisition, ahead of an IPO. Today, the same companies increasingly revisit those questions long after the deal closes. Private equity operating partners are building teams that continuously evaluate portfolio companies using the same agentic tooling they used pre-acquisition. Boards are asking for deeper visibility into AI initiatives before approving additional investment, and internal audit functions are expanding their own reviews to cover AI governance and provider dependencies.

Diligence is becoming less of a milestone and more of a management practice, and that distinction matters. If your organization only becomes diligence-ready when a transaction appears on the horizon, you’re already behind. In practice, what makes a company easy to diligence is usually what makes it easier to run day to day, whether or not a deal is on the calendar. Good governance isn’t something you create for investors. It’s something investors recognize.

Your First Diligence Reviewer May Not Be Human

There’s another change getting far less attention. Historically, engineers wrote technical documentation for expert readers who could infer intent from incomplete documentation and fill gaps through conversation. Increasingly, the first reviewer of your technology isn’t a person at all. Agentic diligence tools can ingest thousands of documents, diagrams, contracts, and repositories in hours rather than weeks. They don’t remember hallway conversations, and they don’t know that “everyone understands how this service works.” They evaluate what’s documented, and nothing more.

That’s a real shift for technical due diligence readiness. For years, incomplete documentation slowed diligence down. Today, incomplete documentation reads as incomplete governance. A human reviewer might spend an hour talking with your team to understand an undocumented AI workflow. But an autonomous reviewer just logs the gap and moves on. Machine-readable governance is becoming just as important as machine-readable code.

Governance Is Becoming the New Technical Moat

This is why the conversation has shifted. The hard problems are no longer limited to distributed systems, cloud architecture, or software delivery. Who approved this autonomous workflow? Who reviews AI-assisted code before it reaches production? What happens if a model provider changes pricing, terms, or availability overnight? Who owns an agent whose decisions span multiple business functions? How quickly can the organization explain a critical AI decision six months later? These aren’t purely technical questions anymore. They’re governance questions, and governance is increasingly what separates organizations that inspire confidence from the ones that generate uncertainty.

That doesn’t mean engineering suddenly matters less. Instead, it means the job expanded. The modern CTO isn’t just responsible for building reliable systems. They’re responsible for building organizations that can explain those systems, at any company stage, to someone who wasn’t in the room when the team made the decisions.

What Modern Technical Due Diligence Readiness Measures

That shift changes what “ready” means. A clean codebase is no longer enough. Neither is a polished architecture deck or a well-organized data room. Modern technical due diligence readiness increasingly measures whether an organization has built the governance structures needed to operate autonomous technology responsibly over time: who reviews AI-generated code, who manages model dependencies, who quantifies technical debt, who monitors autonomous systems, and, most importantly, who remains accountable as machines make more of the operational decisions. The question isn’t whether your AI works. It’s whether your organization can explain why it works, how it’s governed, and what happens when it doesn’t. That’s where the most common governance gaps start to show, and they show up the same way at a Series B startup and a fifteen-year-old enterprise.

Five Governance Gaps Modern Diligence Exposes

Every engineering organization has technical debt and legacy decisions it would make differently today. Still, that’s normal, and it isn’t what sinks a review. Instead, the companies that struggle in technical due diligence readiness reviews are the ones that can’t explain what they have, why it exists, or who’s responsible for it. The same five gaps show up again and again across startups, PE-backed companies, and mature enterprises, a pattern that comes up in Hoola Hoop’s CTO Roundtable series, under Chatham House rules.

The Pattern Behind Every Finding

At first glance, ownership, technical debt, code review, provider dependencies, and unit economics look like five unrelated problems. They’re actually symptoms of the same one: governance. Every one of these findings comes back to four questions. Can your organization explain what exists, and why? Does it know who’s accountable? And can it explain what happens when something changes? Those aren’t engineering questions. They’re organizational questions, and they’re becoming some of the most important technical questions a CTO answers, whatever stage the company is at.

What Technical Due Diligence Readiness Looks Like in Practice

Closing these gaps rarely requires rewriting your platform. Most organizations already have the knowledge. What’s missing are the artifacts: the documentation, the named ownership, the operational discipline that turns what your team knows into something a stranger can verify in two weeks. So organizations that handle technical due diligence readiness well tend to share six governance artifacts, and they didn’t build them for investors. They built them to run the business better.

The Underlying Principle

Governance isn’t documentation, and it never has been. Technical due diligence readiness has always rewarded organizations that can demonstrate preparedness, whether the reviewer is a partner at a PE firm, a board member, or an agent reading your data room at two in the morning. Still, none of this replaces the fundamentals. If you haven’t documented your architecture decisions or priced your technical debt yet, my original technical due diligence guide is still the right starting point. What’s changed since I wrote it is the layer on top: agent governance, model provenance, and AI unit economics are now part of the same conversation, not a separate one.

Questions to Sit With

Whether your next diligence event is a term sheet, an annual portfolio review, or a board asking hard questions ahead of an acquisition you’re making, these are worth working through honestly now, before someone else asks:

• Can you name a single accountable owner for every autonomous system in production right now?
• Is your technical debt register priced in business terms, or still written in engineering language nobody outside the team can act on?
• If a diligence team, human or agentic, asked for your agent inventory tomorrow, does that document already exist?
• What happens to your core AI features if your primary model provider changes pricing or shuts off access next quarter, and is that answer written down anywhere?
• Has your team actually rehearsed your operational playbook for an AI failure, or does it only exist on paper?

A Final Thought

Technical due diligence readiness in 2026 isn’t a favor you do for a future buyer. It’s the same operating discipline that makes a CTO’s own job easier: knowing what your systems cost, who’s accountable for them, and what happens when something breaks. A deal or a portfolio review doesn’t create that need. Instead, it just puts a deadline on work that was already worth doing, whether you’re three weeks from a term sheet or three years past your last one.

I’ve sat on both sides of this table, as the CTO walking a company through an acquisition and as the person building the governance case from the inside. In fact, most of what a 2026 technical review flags isn’t a surprise to the engineering team. It’s a fact everyone already knew, and had never written down where anyone else could find it.

Leigh Newsome

Partner, Hoola Hoop · CTO Coach

Leigh Newsome is a Partner at Hoola Hoop and a CTO coach with 25 years of experience scaling product and engineering teams. He has worked with a wide range of startups and global enterprises, including Avid, Digidesign, WPP, and Kantar/Millward Brown, and successfully led TargetSpot (backed by Union Square Ventures, Bain Capital Ventures, and CBS) through its acquisition to Radionomy Group (Vivendi). When he’s not coaching CTOs, you’ll find him teaching digital audio to graduate students at NYU, building audio and signal processing applications, or flying fixed-wing aircraft, but never all three at once.

Every CTO needs a vibe coding policy now, because the question it answers has already arrived in your repository. None of the people producing this code report to engineering, and the instinctive responses, a quiet ban or a shrug, both fail in predictable ways. I have watched CEOs drop AI-generated code into a channel and announce, “Let’s launch this today, I vibed it with Claude Code so I know it’s solid.” At our June 11 CTO roundtable on agentic adoption, the group of 7 CTOs converged on a principle that holds up better than either: bring demos, not code. This article unpacks what that vibe coding policy means in practice and how to turn four words into an actual operating policy.

Everyone Can Produce Code Now

The numbers describe a transition that is already complete. A June 2026 Black Duck survey of 831 software engineers and DevOps professionals found that 97 percent of development teams use AI coding assistants, while only 30 percent operate under a fully governed approach. A quarter of teams have no defined AI coding policy at all. The activity is not confined to engineering either. A Varonis analysis of 1,000 real-world IT environments found that 98 percent of organizations have unsanctioned or unverified apps in use, including shadow AI. The tools are already inside the building, whether or not anyone approved them.

This is not only a CTO’s problem. Every unreviewed tool a non-engineer ships becomes new attack surface, and that lands on the CISO’s desk as squarely as it lands on yours. The same Black Duck survey found nearly two-thirds of teams are already worried their AI assistants are introducing security defects. Who may ship code has quietly become a security question, not just an engineering one.

The boundary that used to separate “people who write code” from “people who request code” was enforced by skill scarcity. AI removed the enforcement mechanism, and no committee voted on it. In some cases, it seems as if CEOs and BOD members voted on it.

So the CTO inherits a question that did not exist three years ago: what happens when a non-engineer shows up with working code? Most leaders I talk with have answered it by default rather than by design. That is the gap a deliberate vibe coding policy needs to close.

Five Ways the Default Vibe Coding Policy Fails

When there is no deliberate vibe coding policy, organizations fall into one of five default patterns. Each one feels reasonable in the moment. Each one fails for the same underlying reason.

The Common Thread

Each failure comes from treating this as a code question when it is an ownership question. None of the five patterns answers the only thing that matters: who is accountable for what runs in production? A workable vibe coding policy starts there, not at the tooling layer.

What “Bring Demos, Not Code” Means in Practice

The four words carry a precise meaning. Anyone in the company may bring engineering a problem, and anyone may bring a demo that shows the problem and a candidate solution. What they may not bring is a contribution, meaning code that expects to ship as written. A strong vibe coding policy makes that distinction explicit through five commitments.

1. Demos, or even prototypes, are specifications and not contributions.

   A working prototype from a PM is the richest spec engineering has ever received. It encodes intent, the edge cases the PM cares about, and a tested interaction model. Treat it as input to the build, never as the build itself. The artifact informs the work; it does not become the work.

2. Engineering owns everything that ships.

   No exceptions by seniority or title. If it reaches production, an engineering team owns and operates it, and the CTO answers for it. In practice this is a line the CTO and CISO draw together: security owns the bar that anything reaching production must clear, regardless of who built it or how impressive the demo was. Teams that formalize oversight report major efficiency gains at twice the rate of ungoverned teams.

3. Problems get a real intake path.

   A principle without a doorway becomes a ban in disguise. Non-engineers need a defined place to bring demos and prototypes, a named reviewer rotation rather than a single hero, and a committed response time. If the path is slower than going around it, people will go around it.

4. “Production-ready” is written down.

   Security review, test coverage, observability, data handling, dependency policy. The bar does not move based on who built the demo. Publishing the bar does two things: it protects the codebase, and it shows contributors that the gap between a demo and a product is real rather than political.

5. Credit flows to the problem-finder.

   When the shipped feature traces back to a designer’s demo, the designer’s name stays attached. If contributors lose authorship the moment engineering touches the work, they stop bringing it, and the shadow pipeline reopens. Recognition is the cheapest governance tool you have.

The Underlying Principle of This Vibe Coding Policy

The policy works because it trades a false boundary for a true one. The false boundary said only engineers may create code, which is no longer enforceable. The true boundary says only the accountable team may ship code, which is enforceable forever because it rests on responsibility rather than capability.

The Policy Skeleton: Making It Real in Your Org

Writing the principle into operations takes less effort than most governance projects because it follows rails your org already has. The roundtable discussion surfaced a skeleton that adapts to most mid-size and enterprise teams. It starts with classifying every non-engineer artifact into one of three buckets.

Then set the review economics. The reviewer’s job is not to grade the demo’s code, which will usually be discarded. The job is to extract the specification: what problem, what data, what edge cases, what interaction model. A thirty-minute structured review of a working demo can replace what used to be weeks of requirements meetings. That reframe matters, because reviewers who think they are doing code review on AI output burn out fast, and reviewers who understand they are harvesting specs see the value.

Finally, decide where your line sits. The right boundary differs by company. A fintech moving regulated money draws the production-intent line lower than a media company shipping landing pages. This calibration, where the line goes and what it costs to put it in the wrong place, is a judgment call, and it is exactly the kind of decision where operators who have carried the pager tend to see around corners that a generic framework cannot.

Questions to Sit With

1. If a PM opened a pull request tomorrow, does your team have an answer, or just a reaction?
2. How many AI-built tools are running in your company right now that engineering has never seen?
3. What does your written definition of production-ready say, and could a non-engineer find it?
4. Who reviewed the last prototype an executive built, and what happened to their enthusiasm afterward?
5. If the answer to a contribution is “not like this,” does your process show the contributor what “like this” would mean?

A Final Thought

The arrival of non-engineer code is not a crisis. It is the first time in software history that the people closest to the problems can show engineering what they mean instead of describing it in a document. A CTO who meets that with a clear vibe coding policy converts a governance headache into the highest-bandwidth requirements channel the org has ever had. A CTO who meets it with a ban or a shrug gets the shadow pipeline, the rework, and the morale bill.

The work is deciding where your line sits before the next pull request forces the decision in public. This connects to the broader discipline of managing AI-era liabilities deliberately. Our guide on AI governance covers the cost side of the same conversation, and the companion piece on the five things every CTO must do in the agentic era sets the wider context for this vibe coding policy.

Leigh Newsome

Partner, Hoola Hoop

Leigh Newsome is a Partner at Hoola Hoop and a CTO coach with 25 years of experience scaling product and engineering teams. He has worked with a wide range of startups and global enterprises, including Avid, Digidesign, WPP, and Kantar/Millward Brown, and successfully led TargetSpot (backed by Union Square Ventures, Bain Capital Ventures, and CBS) through its acquisition to Radionomy Group (Vivendi). When he’s not coaching CTOs, you’ll find him teaching digital audio to graduate students at NYU, building audio and signal processing applications, or flying fixed-wing aircraft, but never all three at once.

I learned to fly fixed-wing aircraft in California years before I was a CTO, and the two passions keep running into each other in unexpected ways. The clearest example right now is the conversation every CTO I work with is having about agentic AI autonomy. Much of the industry has converged on a five-level framework borrowed from self-driving cars (Swarmia, Tessl, and a half-dozen engineering blogs have all published a version). The framework is fine, but there is a more important part of the conversation that is missing.

The right conversation, the one almost no one is having, is what happens to the human in the seat as the autonomy ratchets up. In the cockpit, the answer to that question was worked out painfully over four decades of accidents, near-misses, and re-trained crews. The answer is not that autopilot replaces the pilot. The answer is that autopilot redefines what the pilot is accountable for, and almost universally raises the stakes of every remaining human decision. The same thing is happening today to the CTO and CPO role. It is worth slowing down enough to see it clearly.

The Lesson the Cockpit Already Learned

When the first generation of glass cockpits and flight management systems landed in commercial aviation in the 1980s, the assumption was the obvious one: a more capable autopilot means a less demanding job for the human. The pilots who lived through that transition will tell you the opposite happened. The hand-flying portion of the job shrank. The judgment portion grew. The number of ways a flight could go subtly wrong, before anyone noticed, expanded as the automation took on more of the routine work.

American Airlines highlighted this challenge in the now-famous 1997 training presentation “Children of the Magenta Line,” delivered by Captain Warren VanderBurgh. The presentation warned that as cockpit automation became more capable, pilots could become overly dependent on flight-management systems and less attentive to the broader operational picture. The lesson was not that automation should be avoided. Rather, pilots needed to understand the appropriate level of automation for a given situation, recognize when the system was no longer helping, and be prepared to step down a level – or fly manually – when necessary. This philosophy became an influential part of modern airline training, emphasizing that automation changes the pilot’s role from continuously controlling the aircraft to continuously understanding, monitoring, and managing it.

Air France 447, lost over the South Atlantic in 2009, remains one of the most frequently cited case studies in discussions of automation and human performance. When unreliable airspeed indications caused the autopilot to disengage, control of a still-flyable aircraft was handed back to the crew under difficult and rapidly changing conditions. The pilots struggled to diagnose the aircraft’s state, recognize an aerodynamic stall, and coordinate an effective recovery. The BEA’s final report has become essential reading for aviation and human-factors professionals because it illustrates how quickly automation failures can become understanding failures. The accident was not caused by a malfunctioning autopilot alone; it exposed the challenges of transferring control, situational awareness, and decision-making from machine to human in a high-pressure environment.

Three Forces That Raise the Leadership Tax

The dominant industry framing assumes that agentic autonomy frees leadership capacity. The opposite is closer to the truth. Three reinforcing dynamics, all of which I’m watching play out in the engineering organizations I coach, push the cost of leadership up as autonomy increases.

The cumulative effect is that the leadership tax goes up with autonomy, not down. The judgment work that used to be distributed across senior reviewers, code review forums, and the slow friction of human throughput now concentrates in fewer hands, fewer decisions, and a much shorter time window. That is not a bad thing. It is the actual job. But it is a different job than most CTOs were promoted into, and it is one nobody is teaching out loud.

Four Decisions a CTO Never Hands to Autopilot

Every cockpit checklist has a category of items that never come off the captain’s plate, no matter how capable the automation gets. Takeoff and landing briefings. The go-around decision. The diversion call. Anything involving an irreversible commitment of fuel, time, or the safety of the people in the back. These are not on the autopilot’s menu because they are not the autopilot’s job. The same logic applies to the CTO seat. Below are the four decisions I coach every technology leader to keep on a written, visible, never-delegate list.

The act of writing this list down, and reading it to your leadership team out loud, is one of the highest-leverage exercises I run with the CTOs and CPOs. It is also one of the most uncomfortable, because every line on the list is a place a leader has historically been tempted to let drift to the team, the platform, or the tooling. The drift is what produces the headlines.

Agentic AI Autonomy Demands More Leadership, Not Less

BCG’s Widening AI Value Gap report, based on a survey of 1,250 senior executives, found that only 5% of organizations are “future-built” on AI, while 60% remain laggards. The difference isn’t budget. BCG argues that leaders adopt an AI-first operating model centered on reinvention rather than incremental investment.

The organizations capturing the most value consistently do three things. First, they add new roles rather than remove them, common examples in AI-native teams include the AI Reliability Engineer, Spec Author, and Agent Orchestrator (see The AI-Native Team). Second, they spend more time on calibration, trust reviews, and retrospectives than on traditional oversight. Third, they redesign organizations around judgment rather than throughput, pairing a flatter execution layer with a denser judgment layer.

The Pre-Flight Checklist

If you are a CTO or CPO running an agentic deployment of any meaningful scale, these are the questions I would put on the page in front of you before the next leadership offsite. They are written as a checklist because a checklist is what works under load, and the next eighteen months of this role will be exactly that.

• Is your trust envelope written down, signed by the right humans, and reviewed on a cadence the team can recite from memory?
• Do the people on your team know what triggers a takeover, who calls it, and what the first three moves are once it is called?
• If an agent did something material wrong this week, can you name the human who answers for it without consulting an org chart?
• Is your never-delegate list a written artifact your direct reports can quote back to you, or is it living only in your head?
• Are you spending more leadership time on calibration and retrospectives than you were a year ago, or are you assuming the agents absorbed the work?

A Final Thought from the Left Seat

The CTOs and CPOs I work with who are getting agentic AI right are not the ones who happen to have the best models, the deepest budget, or the most aggressive rollout schedule. They are the ones who sat down on a Wednesday morning and wrote the trust envelope, the override criteria, the accountability ledger, and the never-delegate list. Then they read them out loud to the team. Then they reviewed them in 90 days. That is not exotic work. It is the work the autopilot cannot do for you.

If you found this useful and want the companion piece on how this connects to the broader board-level conversation about AI accountability, the principles in our agentic AI governance framework pair directly with the never-delegate list above. The two pieces work together: one names the leadership decisions that cannot be automated, the other names the organizational infrastructure required to enforce them.

Leigh Newsome

Partner, Hoola Hoop · CTO Coach

Leigh Newsome is a Partner at Hoola Hoop and a CTO coach with 25 years of experience scaling product and engineering teams. He has worked with a wide range of startups and global enterprises, including Avid, Digidesign, WPP, and Kantar/Millward Brown, and successfully led TargetSpot (backed by Union Square Ventures, Bain Capital Ventures, and CBS) through its acquisition to Radionomy Group (Vivendi). When he’s not coaching CTOs, you’ll find him teaching digital audio to graduate students at NYU, building audio and signal processing applications, or flying fixed-wing aircraft, but never all three at once.