The Year DevOps Grew Up: A Personal Look at 2025's Hard Lessons
Remember When We Thought We Could Just Move Fast?
I was sitting in yet another "platform migration kickoff" meeting in January, watching a slick consultant promise we'd be done by August. The PowerPoint slides showed a beautiful future where all our systems would talk to each other, security would be automated, and AI would handle the boring stuff.
Fast forward to August. That same consultant was explaining why we were 200% over budget and six months behind schedule. The room was full of tired engineers who'd been working weekends trying to clean up data that turned out to be way messier than anyone expected.
That's 2025 in a nutshell: the year DevOps stopped being about moving fast and started being about building things that actually last.
January: When Security Stopped Being Optional
The year started with a wake-up call. On January 3rd, CISA published new Zero Trust guidance that basically said: "Perimeter security is dead. Stop pretending your firewall protects you."
For those of us supporting Navy systems, this wasn't news—we'd been hearing about zero trust for years. But suddenly it went from "nice to have" to "you will do this or lose your contract."
I remember the scramble. Every system security plan needed updates. Every compliance assessment needed re-validation. And with CMMC enforcement coming in November, we didn't have time to waste.
The funny thing? The technical part wasn't that hard. Modern tools exist. The hard part was organizational: getting everyone to agree on identity management, access controls, and who gets to decide what. Turns out, governance is harder than technology.
The DeepSeek Shock: When AI Got Cheap
I'll never forget the day DeepSeek-R1 dropped. I was prepping for a budget meeting, trying to justify why we needed to pay premium prices for OpenAI's models. Then my phone started blowing up.
A Chinese lab no one had heard of released a model that matched OpenAI's best reasoning at 95% lower cost. Nvidia's stock tanked $600 billion in a day.
Suddenly, projects that didn't make financial sense six months earlier became viable. Procurement conversations shifted from "can we afford this?" to "which vendor gives us the best deal?"
The irony? DeepSeek used export-compliant chips—the ones we thought would give American companies an advantage. Clever architecture beat hardware limitations faster than policy could adapt.
Spring: When AI Agents Got Their Own ID Cards
By late January, we faced a weird problem: if AI agents are making decisions and accessing classified systems, how do we audit them? Traditional security assumes human operators. AI agents aren't human.
The solution was both brilliant and slightly absurd: Non-Person Entity (NPE) identities. Basically, CAC cards for AI agents.
I saw this first deployed in logistics systems. AI agents scheduling ship movements needed access to classified databases. The NPE framework gave them auditable identities without requiring a human to be present for every action.
It felt like science fiction: watching an AI agent with its own cryptographic credentials making decisions that would normally require a cleared human. By year's end, this became standard for any AI integration in classified environments.
The Platform Migration Disaster
August brought the reckoning we all saw coming. Research showed 57% of enterprise platform consolidation projects exceeded budgets by 30% or more. 41% missed deadlines by six months or more.
I wasn't surprised. I'd been watching it happen in real time.
The failure modes were always the same:
- Underestimating data quality issues by 10x
- Treating technical migration as the project instead of a small part of organizational change
- Locking into vendor platforms without exit strategies
- Failing to establish who gets to make decisions before deployment
The lesson was painful but clear: platform consolidation isn't a technology project. It's organizational transformation that happens to involve technology.
When AI Started Running the Show
The biggest shift this year was watching AI move from "copilot helping humans" to "autonomous agent doing the work."
In January, AI agents needed their own identities. By March, sovereign LLMs were running on classified networks. In June, edge AI provided tactical capabilities without network connectivity. By August, AI agents were managing data governance without human oversight. In November, Microsoft Agent 365 gave us a control plane for enterprise AI agents.
I experienced this transition firsthand. Early in the year, my team was manually provisioning infrastructure. By December, we were defining policies and watching AI agents handle the provisioning.
The work became more strategic, less tactical. Instead of writing Terraform, we were supervising agents that wrote Terraform.
The governance challenge was real: how do you ensure agents behave correctly without reviewing every action? We learned the hard way that agents need:
- Comprehensive testing
- Observability platforms tracking their decisions
- Circuit breakers limiting their authority
- Audit trails for accountability
Organizations that solved agent governance saw huge productivity gains. Organizations that didn't experienced spectacular failures—agents optimizing for the wrong objectives, violating policies, or breaking systems through unexpected interactions.
CMMC: The Compliance Wake-Up Call
November 10th was D-Day for defense contractors. CMMC 2.0 Phase 1 became effective, meaning contractors handling Controlled Unclassified Information needed third-party certification to bid on DoD contracts.
Thousands of contractors discovered their self-attestations weren't enough. Many failed initial assessments.
I supported Navy contractors through this process. The common failures:
- Treating CMMC as a checklist instead of systemic security improvement
- Implementing controls without understanding the requirements
- Failing to document policies and procedures
- Not maintaining continuous compliance between assessments
The organizations that succeeded treated CMMC as a forcing function for DevSecOps maturity: automated compliance checking, infrastructure as code with security baked in, continuous monitoring with automated alerting.
The Skillset Shift: From Operators to Policy Designers
This was the year DevOps jobs changed fundamentally.
Previously: Write scripts, configure pipelines, respond to incidents Now: Design agent behaviors, set governance policies, handle exceptions agents can't resolve
I watched engineers struggle with this transition. The skills that made someone great at operational execution weren't the same skills needed for policy design and agent supervision.
Platform engineering emerged as the discipline that bridges this gap. It's not just about deploying applications quickly—it's about building sustainable operational capability with appropriate governance.
In Navy platforms, I saw the difference firsthand: commands with dedicated platform engineering teams maintained reliable systems. Commands treating DevOps as deployment automation experienced chronic instability.
What Actually Worked (and What Didn't)
What worked:
- Incremental migration: Gradually moving functionality while maintaining legacy systems succeeded. Big bang migrations failed spectacularly.
- Sovereign AI: On-premises LLMs solved the data sovereignty problem for classified systems. Performance was good enough, and operational independence from commercial clouds was strategically valuable.
- Platform engineering teams: Dedicated teams owning long-term platform operations outperformed project-based approaches every time.
- Realistic planning: Organizations that tripled their timeline estimates and doubled their budget estimates succeeded. Optimistic planners failed.
What failed:
- Platform consolidation: 57% budget overruns weren't anomalies—they were predictable consequences of treating organizational transformation as technology projects.
- AI deployment without governance: Early agentic AI deployments without proper frameworks caused operational failures. Agents need policy constraints and observability.
- Vendor lock-in assumptions: Organizations believing vendor promises about migration simplicity suffered. Platform dependencies are real. Exit costs are high.
- Data quality neglect: You can't migrate bad data to good platforms and get good results. Clean data first, migrate second. There are no shortcuts.
The Hard Truths from the Trenches
Supporting Navy ERP systems through 2025's transformations taught me some uncomfortable lessons:
Governance beats technology every time. The fanciest platform with the best architecture fails without clear decision authority, funding models, and enforcement mechanisms. Solve governance first, technology second.
Compliance is a forcing function for maturity. CMMC, FedRAMP, and Zero Trust aren't bureaucratic overhead—they're frameworks driving operational discipline. Organizations that treated compliance as maturity enablers succeeded. Organizations that treated it as checkbox exercises failed.
Agentic AI requires human judgment. Agents excel at routine operations following clear policies. Novel scenarios, trade-off decisions, and exception handling still require humans. Design for agent autonomy within defined boundaries, not universal automation.
Realistic planning prevents disasters. Triple your timeline estimates. Double your budget estimates. Build contingency for discovered complexity. Optimistic planning feels good in kickoff meetings. It feels terrible when you're 200% over budget in year three.
Vendor promises are marketing. Trust but verify. Build exit strategies. Maintain architectural independence. Don't design systems that only work with one vendor's platform.
Looking Ahead to 2026
Based on what I saw this year, here's what I expect in 2026:
More agentic systems at scale: The question shifts from "should we use agents" to "how do we govern thousands of agents."
CMMC Phase 2 expansion: More contractors needing certification, assessment backlogs creating compliance bottlenecks.
Zero trust maturity: Moving beyond network controls to application-level microsegmentation. Identity-based access for every service interaction.
Sovereign AI proliferation: Every defense program requiring on-premises AI capabilities. Commercial cloud dependencies decreasing.
Platform engineering discipline: Formal training programs, certification frameworks, and career paths emerging.
The meta-trend: DevOps is professionalizing. The "move fast and break things" era is ending. Enterprise and defense environments demand operational discipline, governance frameworks, and sustainable practices.
Conclusion: The Discipline Decade Begins
2025 marked the end of DevOps's adolescence. The industry grew up, encountered hard limits, and started rebuilding with appropriate discipline.
Platform migrations failed because organizations lacked governance frameworks. Agentic AI emerged because the technology matured and governance frameworks provided deployment models. CMMC enforcement forced security maturity defense contractors should have implemented years ago.
The common thread: successful DevOps in 2025 required organizational discipline, not just technical capability.
As we enter 2026, the challenges scale: more agentic systems, more complex compliance requirements, more platform consolidation attempts, more organizations learning these lessons the hard way.
For those of us in the trenches—supporting Navy systems, deploying AI on classified networks, migrating legacy platforms—the work continues. Not because it's glamorous. Because it's necessary.
The future of DevOps isn't in the latest AI model or the newest platform. It's in the disciplined application of governance frameworks, operational maturity, and sustainable engineering practices.
That's not inspiring. It's realistic.
And after 2025, realism feels appropriate.
Amyn Porbanderwala is Director of Innovation at Navaide, where he leads AI/ML integration and platform engineering for defense systems. He previously spent eight years supporting Navy ERP modernization efforts across Pacific Fleet, Atlantic Fleet, and CNIC. The views expressed are his own.