The Year Agentic AI Got Real: A Developer's Retrospective on 2025

Fri, 12 Dec 2025 09:00:00 +0000

By the end of 2025, software engineering felt materially different from a year earlier.

In 2024, AI in software development was mostly about autocomplete. We had GitHub Copilot, Cursor, and LLMs that were essentially “very fast search engines with a code editor.”

In 2025, that changed. We moved from autocomplete to autonomy. This was the year Agentic AI started to feel real.

The Three Shifts of 2025

Three key technical and cultural shifts happened this year that changed how we build and deliver software.

1. From “Chat” to “Task”

At the beginning of the year, we were still “chatting” with AI. We had to explain the context, provide the files, and then manually copy the code into our editors.

By mid-2025, tools like Copilot Agent Mode and specialized platforms like CueMarshal shifted the interface. Instead of “Write a function for X,” we started saying “Implement the login flow for the new multi-currency engine.” The AI became responsible for the task, not just the output.

2. The Standardization of “Tools” (MCP)

Before 2025, every agent was its own silo. If you wanted an AI to talk to your database or your issue tracker, you had to write a custom integration.

The introduction and rapid adoption of the Model Context Protocol (MCP) changed that. MCP gave us a common language for agents to interact with the world. It turned every API, every database, and every repo into a structured tool that any agent could use. This was the “USB moment” for AI agents.

3. The Rise of the “Agentic Orchestra”

We realized that one “do-it-all” agent was a dead end. Instead, the high-performing teams started building Agentic Orchestras: specialized agents for architecture, development, testing, and review, all working together within a Git-native workflow.

In my own work with CueMarshal, this shift was the difference between an AI that “helped” me and an AI that “worked” with me.

What 2025 Taught Me

The biggest lesson of 2025 for me is that the human role in software engineering is not disappearing. It is being reframed.

More of us are moving into a conductor-architect role. The job is less about writing every line of code and more about designing the system, defining the constraints, and orchestrating the agents doing the implementation. That requires a deeper understanding of Platform Engineering and system architecture than before.

Looking Ahead to 2026

If 2025 was the year of “autonomy,” I expect 2026 to be the year of reliability. We have the tools. We have the protocols. Now we need governance and observability that make agentic workflows as predictable as a standard CI/CD pipeline.

The transition is already underway, and it is moving faster than most people expected. The question for 2026 is no longer “Will you use AI?” It is “How will you orchestrate your agents?”

Related reading:

Beyond CI/CD: Why AI Agents Are the Next Layer of Software Delivery

Wed, 18 Jun 2025 09:00:00 +0000

For the last decade, “modern” software delivery has been defined by CI/CD. We spent years perfecting the art of the deterministic pipeline: code goes in, tests run, artifacts build, and deployments happen. If a human pushes a button (or a branch), the machine follows a script.

It works. It is stable. It is the foundation we all build on.

But CI/CD has a ceiling. It handles the delivery of the code, but it is blind to the coordination of the work. The bottleneck for most engineering teams is no longer how fast the build runs; it is how long an issue sits unassigned, how many times a PR bounces back for minor linting errors, and how much context is lost between a design doc and an implementation.

The next layer of software delivery isn’t just “faster CI.” It is Agentic Delivery.

The Coordination Gap

If you look at a typical engineering day, the actual “delivery” (the pipeline run) is a tiny fraction of the cycle. Most of the time is spent in the coordination gap:

Context Assembly: Figuring out which files matter for a task and what the existing patterns are.
Quality Gating: Waiting for a human to notice a typo, a missing test, or a broken internal convention.
Context Handoff: Writing the PR description, explaining the why to a reviewer, and updating the docs.

Traditional CI/CD tools cannot help here because they are stateless and reactive. They don’t “understand” the task; they only know if the exit code was zero.

Enter the Agentic Layer

When I started building CueMarshal, I realized that the real value of AI agents in DevOps isn’t just generating code. It is handling the non-deterministic parts of the workflow that humans find tedious but necessary.

An agentic delivery layer lives above your CI/CD. It doesn’t replace Jenkins or GitHub Actions; it uses them.

What an Agentic Layer actually does:

Active Monitoring: Instead of waiting for a manual trigger, agents can watch for new issues, draft an implementation plan, and open a PR before a human even context-switches.
Semantic Quality Gates: While a linter checks syntax, an agent can check if the implementation actually matches the requirements in the issue.
Continuous Context Maintenance: Agents can keep documentation in sync with code changes in real-time, effectively eliminating “doc debt.”

Lessons from the Trenches

Building this into my own platform taught me that the transition from CI/CD to Agentic Delivery requires a shift in how we think about automation.

From Scripts to Tools: In CI/CD, we write shell scripts. In agentic systems, we define Model Context Protocol (MCP) tools. The agent needs a structured way to interact with the world, not just a line of bash.
From Success/Failure to “Looks Good to Me”: We are moving from binary pass/fail gates to probabilistic evaluation. That is why specialized agent roles matter so much: you want a dedicated “Reviewer” agent whose only job is to be skeptical.
Git is still the anchor: Even with agents doing the heavy lifting, the final source of truth must remain the repository. If it’s not in Git, it didn’t happen.

The 2025 Reality

We are past the “AI hype” phase where we wonder if agents can do this. In 2025, the question for engineering leaders is how to integrate them without breaking the trust and safety of the delivery pipeline.

The teams that win won’t be the ones that let AI run wild. They will be the ones that build an agentic layer that respects the rigor of Platform Engineering while reclaiming the hours lost to the coordination gap.