Insights · June 11th, 2026
For fifty years, software has worked the same way: humans break down a problem, write the decision-making logic into code, and then maintain that code by hand as the world changes. A recent paper argues that AI agents break this model entirely—not by making coding faster, but by changing what software fundamentally is. The argument is provocative and, in places, speculative. But the strategic questions it raises are real, and they belong on the executive agenda now.
Summary of Key Findings and Discussion Points
The paper’s central claim is a distinction worth understanding. In traditional software, the code carries the decision logic—every rule must be written by an engineer before the system ever runs. In an agentic system, a large language model is the reasoning engine, and it generates code at runtime as a disposable tool, then discards it. The author’s framing: the agent isn’t using software, the agent is the software. Code stops being the product and becomes scratch paper.
This is positioned as the third major shift in how software is delivered. The first generation (licensed, on-premise software like Oracle) put the burden of installation and maintenance on the customer. The second (SaaS like Salesforce) moved that complexity to the vendor’s cloud and shifted billing to subscriptions. The third—what the author calls Agent-as-a-Service (AaaS)—moves something more profound to the provider: not just operational complexity, but decision-making complexity itself. The customer no longer specifies how a result should be produced. They specify what outcome they want, and pricing moves toward paying per result delivered.
The discussion section is refreshingly honest about limits. The paper cites encouraging benchmarks—open models resolving roughly 30% of real GitHub issues, and one enterprise pilot cutting root-cause debugging time by 93% through coordinating multiple agents rather than using one better agent. But it gives equal weight to a sobering counterpoint: the EvoClaw benchmark, where agent performance collapses from over 80% on isolated, well-scoped tasks to at most 38% when asked to sustain development over time—maintaining a system across many changes where errors accumulate. The author names four reasons: agents lose track of the big picture as codebases exceed their memory (“context drift”); small early mistakes cascade; agents don’t weigh the long-term cost of shortcuts (“technical debt”); and automated testing can pass while subtle bugs slip through.
The honest takeaway is that agentic AI is genuinely transformative today as an augmentation tool, but fully autonomous software development remains a multi-year research problem.
The paper sketches a four-stage roadmap—from today’s coding assistants, to agents owning single tasks end-to-end, to coordinated multi-agent “teams” mirroring an engineering org, to self-improving ecosystems—with the later stages explicitly aspirational.
Why It Matters
Three implications stand out for anyone running a business.
The economics of building software products flip. If outcomes can be purchased rather than systems built and maintained, the long tail of custom software—internal tools, integrations, workflow glue—becomes dramatically cheaper to obtain and faster to change. The competitive moat of “we built proprietary software for this” weakens when a competitor can have an agent produce a working equivalent on demand. Conversely, if your business sells software, outcome-based pricing pressures the subscription model that may underpin your revenue.
The bottleneck moves from time to build to time for verification and security. In the “AI helps engineers code faster” model, the human stays on the critical path for design, integration, and verification—so the gains are capped. The agentic model aims to remove the human from execution entirely, leaving them to set intent and audit results. Where that works, the constraint on output shifts from headcount to the quality of your goals, your evaluation criteria, and your oversight. Organizations that figure out how to direct agents well, not just deploy them, capture the upside.
The risk profile changes shape, and it’s unpredictable (mostly). The EvoClaw findings are the most important part of the paper for executives. The failure mode of agentic systems is not “it doesn’t work”—it’s “it works impressively in a demo and degrades quietly over time.” Errors compound, tests give false comfort, and no one is accountable in the traditional sense. That is a governance and liability problem, not just an engineering one.
What This Means for CEOs
This is a moment for ambitious but calibrated action—not a wholesale bet, and not waiting on the sidelines. Practical priorities:
- Start where the criteria are clear. The best early candidates for agent automation are tasks with well-defined success conditions, contained scope, and existing tests—debugging, routine feature work, data tasks. Avoid pointing agents at sprawling, mission-critical systems where errors silently accumulate.
- Invest in evaluation, not just adoption. The quality of agent output is only as good as your ability to judge it. Building rigorous test suites and outcome metrics is now a strategic capability, because it’s what lets you safely give agents more autonomy over time. This is where much of the durable advantage will sit.
- Treat oversight as a real role. Adopt a posture the paper frames well: agents own execution, humans own intent, judgment, and ethical guardrails. That means new accountability for who specifies goals, who audits outputs, and who is answerable when an agent’s work fails downstream.
- Rethink team shape deliberately. If individual productivity multiplies through agent leverage, smaller teams of skilled “orchestrators” may do what larger teams did before. That has implications for hiring, the skills you value, and how you develop people—the premium shifts from writing code to articulating intent clearly and designing how agents coordinate.
- Watch your own business model. If you sell software or services, ask how outcome-based, agent-delivered competition would price against you, and where your defensibility actually lies—data, distribution, trust, regulatory position—once building the software gets cheap.
The most useful framing for a CEO is not “is software ending?” It isn’t. It’s that the unit of value is migrating from the system you build to the outcome you specify and verify. Companies that build the muscles to direct and audit autonomous work—while staying clear-eyed about where these systems still break—will be positioned to lead the transition rather than absorb it.
This is an executive brief based on “Agentic Software: How AI Agents Are Restructuring the Software Paradigm” (Cao, June 2026) – read here
A word of caution on sourcing: this is a single-author argument from a private firm, not independent research, and it leans on its own framing. The benchmark data points are real and checkable; the roadmap timelines and the grander “software redefined” thesis are interpretations. Read it as a well-reasoned hypothesis, not a verdict.
Read other articles in the series:
The CEO’s guide to AI: The Pope Has an AI Strategy Memo for Your Boardroom
The CEO’s guide to AI: State Media Control Influences Large Language Models
The CEO’s guide to AI: Social engineering is turbocharged by AI
The CEO’s guide to AI: Black-hat LLMs and Cyber-threats
The CEO’s guide to AI: We’re nowhere near AGI
The CEO’s guide to AI: Young workers as the canaries in the coalmine
The CEO’s guide to AI: The risk of vibe coding complacency
The CEO’s guide to AI: The Case for Superhuman Adaptable Intelligence (SAI)
About Nikolas Badminton
Nikolas Badminton is the Chief Futurist & Hope Engineer at futurist.com. He’s a world-renowned futurist keynote speaker, consultant, author, media producer, and executive advisor that has spoken to, and worked with, over 500 of the world’s most impactful organizations and governments.
Nikolas is an artificial intelligence expert and his 2026 keynote ‘The AI Leader: Create Incredible Productivity, Profit & Growth’ is the level up for the modern CEO and executive leader.
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