Tagged: AI

Codify How You Work

You don’t build an agent by thinking about agents. You build an agent by thinking about how you do work.

Your ability to multiply your output begins with a simple discipline: take the skills locked in your head and turn them into structured, repeatable workflows. This is the starting point for all operational leverage. This is the kernel the entire system will be improved on.

The Way

When you codify how you work, you give yourself a system that can multiply your output and scale across projects, teams, and tools. You create clarity about how decisions get made, how work begins, how it moves, and how it completes. This is the foundation for any AgenticOps system you may build later.

But at this stage, the focus is only on you and the real place you get work done.

Establishing structure creates surface area for improvement. Improvement reduces waste. Waste reduction compounds over time.

This is the quiet logic behind AgenticOps: you externalize your way of working, let the system run your way as is. Then observe the friction and reduce waste where it naturally accumulates. You are not inventing efficiency. You are uncovering it and optimizing it away.

The Problem

Most people never write down how they work. They assume it is too complex, too obvious, or too personal to articulate. Some worry that codification leads to replacement. Some worry that it is tedious or unnecessary.

These fears result in the same outcome. The process remains invisible, so it cannot be measured, analyzed, improved, shared, or extended. Its hard to multiply if you can’t see what to multiply.

Then there are people that write down how they want to work instead of how they work today. Premature optimization is a trap. Clarity first. Compression later.

Solution Overview

The DecoupledLogic way is to treat your workflow and the workflow data as the most valuable operational asset you have. Before any optimization or automation is possible, we capture the real way you move through work. Not the theoretical model. Not the cleaned-up version. Not the one you wish you followed. The one you actually practice when no one is watching.

How you orient yourself. How you define what matters. How you locate the boundaries. How you identify the first irreversible decision. How you choose what not to do. How you set priority and direction before you set pace.

This is the material the system will learn from. This is the kernel it will grow from.

How It Works

All work follows a simple cycle: Start > Work > End. Input > Process > Output. This canonical sequence never changes. It is one of the few timeless rules in operational thinking.

Within the loop there are deeper patterns that matter.

Getting ready

This is how you select the next thing to work on. How you prioritize. How you set a target or goal. How you define the expected outcome and your stopping point. How you establish your north star. This is also where you gather context, align resources, and prepare your operational environment. Getting ready is not passive. It is an active decision about where your attention is going and why. This could be a simple 10 second though, don’t make it overly deep.

Starting the work

This is how you signal the start of the task. How you initiate the first meaningful action. How you reduce uncertainty enough to move forward. How you commit to the direction you set in the previous step. Starting is not the same as preparing. It is the moment you choose momentum over deliberation and take the first step out of the starting blocks.

This is the first move. And that first move is the kernel the entire system will be built on.

Working in flow

This is how you break down the problem. How you evaluate options and make decisions. How you measure progress while you are inside the work. How you prevent stalls and maintain forward motion. This is where your thinking style creates the most value and where codification has the greatest impact.

Ending the work

This is how you decide something is complete. How you package, deliver, publish, or hand off. How you create closure and free cognitive space for the next cycle. Ending well is as important as starting well because it defines what counts as done.

Reviewing the work

This is how you assess quality. How you reflect on what happened. How you identify improvement targets. How you reset for the next iteration of a cycle.

Cross-cutting functions

These patterns show up at every stage. How you communicate the work. How you measure the work. How you improve the work. These are not separate steps. They shape the entire cycle from beginning to end.

You already do all of this consciously or subconsciously. Codification is simply making it visible.

Impact

Once your workflow is explicit:

  • You gain clarity about your own method
  • You reduce waste because you can see where energy leaks
  • You create a pattern others can follow without confusion
  • You unlock automation and agents that actually reflect how you work
  • You build a system that can multiply your output and evolve with you, not around you

A system is only as strong as its kernel. An agent is only as good as the pattern it learns from. And a workflow can only be optimized once the work itself has been made visible.

Start

Do not start building agents by thinking about agents. Start by thinking about how the work is done.

Write down your beginning, your flow, your completion, and your review. Capture your real process in the real place that work gets done. Let the existing system reflect back how it works. Then reduce the waste you can now see in the reflection.

Once the process exists outside the head of the people doing the work, the path to optimization becomes straightforward. Start with how you work.

If you want, we can codify your workflow together and create your first operational blueprint to begin improving how you work in the agentic age.

Let’s talk about it.

Background Agents in Cursor: Cloud-Powered Coding at Scale

Build Faster with Cloud-First Automation

Imagine coding without ever leaving your IDE, delegating repetitive tasks to AI agents running silently in the background. That’s the vision behind Cursor’s new Background Agents, a new feature that brings scalable, cloud-native AI automation directly into your development workflow.

From Local Prompts to Parallel Cloud Execution

In traditional AI pair-programming tools, you’re limited to one interaction at a time. Cursor’s Background Agents break this mold by enabling multiple agents to run concurrently in the cloud, each working on isolated tasks while you stay focused on your core logic.

Whether it’s UI bug fixes, content updates, or inserting reusable components, you can queue tasks, track status, and review results, all from inside Cursor.

Why This Matters

Problem: Manual Context Switching Slows Us Down

Every time we need to fix layout issues, update ads, or create pull requests, we context-switch between the browser, editor, GitHub, and back.

Solution: One-Click Cloud Agents

With Background Agents, we:

  • Offload UI tweaks or content changes in seconds
  • Automatically create and switch to feature branches
  • Review and merge pull requests without leaving the IDE

It’s GitHub Copilot meets DevOps, fully integrated.

How It Works

  1. Enable Background Agents under Settings → Beta in Cursor.
  2. Authenticate GitHub for seamless PR handling.
  3. Snapshot your environment, so the agent can mirror it in the cloud.
  4. Assign tasks visually using screenshots and plain language prompts.
  5. Review results in the control panel with direct PR links.

Each agent operates independently, meaning you can:

  • Fix mobile UI bugs in parallel with adding a new ad card.
  • Update dummy content while another agent links it to a live repo.

Keep tabs on multiple tasks without blocking your main flow.

Note: This is expensive at the moment because it will use the Max Mode.

The Impact: Focus Where It Matters

  • 🚀 Speed: Complete multi-step changes in minutes.
  • 🧠 Context: Stay immersed in Cursor with no GitHub tab juggling.
  • 🤝 Collaboration: Review, update, and deploy changes faster as a team.

What’s Next?

The Cursor team is working on:

  • Auto-merging from Cursor (no GitHub hop)
  • Smarter task context awareness
  • Conflict resolution across overlapping branches

Is this is the future of development workflows, agent-powered, cloud-native, and editor-first?

Try It Out

Enable Background Agents in Cursor and assign your first task. Start with a UI fix or content block update and see how you like it. Just remember that this service uses Max Mode and is expensive so be careful.

If you are looking to improve your development workflow with AI, let’s talk about it.

enterprise_software_is_broken

Enterprise SaaS is Broken. AI Agents Can Fix It.

Let’s talk about enterprise software.

Everyone knows the dirty secret: it’s complex, bloated, slow to change, and ridiculously expensive to customize. It’s a million dollar commitment for a five-year implementation plan that still leaves users with clunky UIs, missing features, and endless integration headaches.

And yet, companies line up for enterprise software as a service (SaaS) products. Why? Because the alternative, building custom systems from scratch, can be even worse.

But what if there was a third way?

I believe there is. And I believe AgenticOps and AI agents are the key to unlocking it.

The Current Limitation: AI Agents Can’t Build Enterprise Systems (Yet)

There’s a widely held belief that AI agents aren’t capable of building and maintaining enterprise software. And let’s be clear: today, that’s mostly true.

Enterprise software isn’t just code. It’s architecture, security, compliance, SLAs, user permissions, complex business rules, and messy integrations. It’s decades of decisions and interdependencies. It requires long-range memory, system-wide awareness, judgment, and stakeholder alignment.

AI agents today can generate CRUD services and unit tests. They can refactor a function or scaffold an API. But they can’t steward a system over time, not without help.

The Disruptive Model: Enterprise System with a Core + Customizable Modules

If I were to build a new enterprise system today, I wouldn’t sell build a monoliths or one-off custom builds.

I’d build a base platform, a composable, API-driven foundation of core services like auth, eventing, rules, workflows, and domain modules (like claims, rating engines, billing, etc. for insurance).

Then, I’d enable intelligent customization through AI agents.

For example, a customer could start with a standard rating engine, then they could ask the system for customizations:

> “Can you add a modifier based on the customer’s loyalty history?”

An agent would take the customization request:

  • Fork the base module.
  • Inject the logic.
  • Update validation rules and documentation.
  • Write test coverage.
  • Submit a merge request into a sandbox or preview environment.

This isn’t theoretical. This is doable today with the right architecture, agent orchestration, and human-in-the-loop oversight.

The Role of AI Agents in This Model

AI agents aren’t building without engineers. They’re replacing repetition. They’re doing the boilerplate, the templating, the tedious tasks that slow innovation to a crawl.

In this AgenticOps model, AI agents act as:

  • Spec interpreters (reading a change request and converting it into code)
  • Module customizers (modifying logic inside a safe boundary)
  • Test authors and validators
  • Deployment orchestrators

Meanwhile, human developers become:

  • Architects of the core platform
  • Stewards of system integrity
  • Reviewers and domain modelers
  • Trainers of the agent workforce

The AI agent doesn’t own the system. But it extends it rapidly, safely, and repeatedly.

This Isn’t Just Faster. It’s a Better Business Model.

What we’re describing is enterprise software as a service as a living organism, not a static product. It adapts, evolves, and molds to each client’s needs without breaking the core.

It means:

  • Shorter sales cycles (“Here’s the base. Let’s customize.”)
  • Lower delivery cost (AI handles the repetitive implementation work)
  • Faster time to value (custom features in days, not quarters)
  • Higher satisfaction (because the system actually does what clients need)
  • Recurring revenue from modules and updates

What It Takes to Pull This Off

To make this AgenticOps model work, we need:

  • A composable platform architecture with contracts at every boundary (OpenAPI, MCP, etc.)
  • Agents trained on domain-specific architecture patterns and rules
  • A human-in-the-loop review system with automated guardrails
  • A way to deploy, test, and validate changes per client
  • Observability, governance, and audit logs for every action an agent takes

Core build with self serve client customizations.

AI Agents Won’t Build Enterprise Software Alone. But They’ll Change the Game for Those Who Do.

In this vision, AI Agents aren’t here to replace engineers. In reality, they may very well replace some engineers, but they could also increase the need for more engineers to manage this agent workforce. Today, AI Agents can equip engineers and make them faster, freer, and more focused on the work that actually moves the needle.

This is the future: enterprise SaaS that starts composable, stays governable, and evolves continuously to meet client needs with AI-augmented teams.

If you’re building this kind of Agentic system, or want to, let’s talk about it.

AI Agents Can Write Code, Here’s How We Win as Developers

This is a thought exercise and a biased prediction. I have no real facts except what I see happening in the news and observed through my experiences. I don’t have any proof to back up some of my predictions about the future. So, feel free to disagree. Challenge my position, especially when I try to blow up the rockets in the end.

The Game has Changed

We don’t need to write C#, Python, or Java to build software anymore. Just like we no longer need to code in assembly or binary, today’s high-level languages are now being pushed down a level. We can code by talking to an AI agent in plain English. This isn’t science fiction. AI agents are here, and they’re disrupting traditional software development. The value isn’t in writing code, it’s in delivering value and desired business outcomes.

Soon, every app can be basically copied by an agent. Features don’t matter, value does. This means the future isn’t about who can write the best code or build the best feature set. Any product developer with an agent worth its silicon will be able to write an app. For product developers, it will soon be about who can use AI agents in a way that actually delivers business value. Those developers that have Agentic: Ops, Design, Development, Infrastructure, and Marketing will beat those without. Those with agents and experienced agent operators that deliver value rapidly will beat the developers that still take 3 months to deliver an MVP.

AI is No Longer Just a Tool, It’s the New Coder

AI assistants won’t just be assisting developers, as I once thought, they will become the developers, the designers, the marketers, the project managers. The shift isn’t about writing code faster. It’s about not writing code at all and letting AI generate, deploy, and optimize entire systems. How do we manage AI agent employees? An AI HR agent? The implications are far wider than just the replacement of humans in developer roles. Markets are going to shift, industries will be disrupted regularly, the world is going to enter a new age faster than any other shift in civilization that we’ve had in the past. I may be wrong, but it looks clear to me.

What does that mean for us?

  • The focus moves from software development to AI agent development and integration.
  • Companies that figure out how to deliver value with agents effectively will dominate product development.
  • The winners will have an early advantage building a proven system, with tested agents, and experienced agent operators that customers will trust to continuously deliver desired value.

Product Features are Dead, Value Delivery is Everything

If an AI agent can copy any feature, what really matters in product development? Value delivery, that’s what. Value has always been king and queen in product development. I believe it’s more important now than ever. AI-native product developers will outperform traditional ones not only because they don’t waste time or money on manually coding features, but they focus on outcomes and delivering value that deliver those outcomes.

Hell, I’m seeing people that can’t code build apps that use to take weeks to build. They can build an app in 30 minutes, and we are still on v1 baby agents. What happens when the agents grow up in a couple years. In the future time won’t matter because we can deliver apps and features in day. Costs become less of a concern because agents cost less than hiring new employees. Understanding and delivering value will be the great divider between product development teams. Those who can wield agents to understand and deliver value will do better in the market.

China and the team that built DeepSeek proved that they can beat the likes of multi-billion dollar US aligned companies with less than $10 million to train a frontier model. What will someone with a team of agents delivering value in days do against an old school team of human developers delivering the same value in months.

Think about it 🤔

Businesses don’t care if the back end is in Python or Rust. They care if revenue goes up and costs go down.

Customers don’t care if their data is in PostgreSQL or SQL Server. They care if their system is performant and costs are feasible.

Users don’t care if the UI is React or Blazor. They care if the experience is seamless and solves their problems.

No one asks whether an AI agent or human wrote the code, they just want a solution that fills their needs.

A product development team’s value is not in their technology choices but in the value they can deliver and maintain.

The AI-Native Product Development Playbook

If AI replaces traditional software product development, how do we compete? We learn to not focus on coding features, we build AI-driven systems that can deliver value.

Here’s A Playbook 🚀

1. Find the pain points where AI delivers real value. Optimize workflows, automate decisions, eliminate inefficiencies, increase customer attraction, acquisition, engagement, retention, and satisfaction.
2. Use rapid prototyping to test and iterate at breakneck speed. Don’t waste weeks and months building, when we can ship, test, and refine in days.
3. Orchestrate AI agents. Until AI surpasses AGI (artificial general intelligence) and reaches super intelligence, initial success won’t come from using a single agent. It will come from coordinating multiple agents to work together efficiently.
4. Measure and optimize continuously. The job isn’t done when a system is deployed. AI needs constant tuning, monitoring, and retraining.

People Still Want Human Connection

There’s one thing AI agents can’t replace, human relationships. People will always crave trust, emotional intelligence, and real connection with other humans. Businesses that blend AI automation with authentic human experiences will win.

The Future of Software Product Development is AI-First, Human-Led

This isn’t about whether AI will replace traditional software product development or developers. That ship is sailing as we speak, it is underway. The real question is who will successfully integrate and optimize AI in businesses? Who can help build AI-native businesses that out compete their competitors? I hope the answer is you. The future is AI-first. Those who embrace it will lead. Those who resist will be left behind because we are the Borg, resistance is futile.

Now, my last question is, are you ready? Do you know how to transform now? Evolution is too slow. You must blow up some rockets to rapidly figure out what works and doesn’t work. But doing so is easier said than done when jobs and investments are on the line. For now, we may be OK staying stuck in our ways and relying on old thought processes. I’d say we have 5-10 years (into my retirement years) to enjoy the status quo. However, that time horizon seems to shrink every month and every day not focused on transformation is a day lost to the competition.

Need help in your transformation? Let’s talk about the rockets you want to blow up.

The Copilots Are Coming

This is an unpublished throwback from 2023. Obviously, the Copilots are here and its much scarier than I thought.

In “The age of copilots” Satya Nadella, the CEO of Microsoft, outlines the company’s vision for Microsoft Copilot, positioning it as an integral tool across all user interfaces.

Microsoft Copilot
Meet your everyday AI companion for work and life.

https://www.microsoft.com/en-us/copilot

Copilot incorporates search functionality, harnessing the context of the web. This was a genius pivot of Bing Chat into a multi-platform service. They even have an enterprise version with added data protection (they are listening to the streets). And they are giving power to the people, Microsoft 365 now features Copilot, which operates across various applications. As a developer, my Semantic Kernel plugins can be easily integrated, my OpenAI GPTs and Assistants can be integrated. I can build some things, my team can build more things and considering the world currently needs so many Copilot things, I’m so excited. So many tasks to optimize, so many roles to bring efficiency to, so many jobs-to-be-done to be supported by automation and AI.

We believe in a future where they will be a copilot for everyone and everything you do. 

Satya Nadella, CEO of Microsoft

Nadella emphasizes the customizability of Copilot for individual business needs, highlighting its application in different roles. GitHub Copilot aids developers in coding more efficiently, while SecOps teams leverage it for rapid threat response. For sales and customer service, Copilot integrates with CRM systems and agent desktops to enhance performance.

Furthermore, Nadella speaks about the extension of Copilot through the creation of a Copilot Studio, which allows for further role-specific adaptations. He notes the emerging ecosystem around Copilot, with various independent software vendors and customers developing plugins to foster productivity and insights. I hope this means there is a Copilot Store coming with some revenue share with independent software vendors like the me and the company I work for.

You will, of course, need to tailor your Copilot for your very specific needs, your data, your workflows, as well as your security requirements. No two business processes, no two companies are going to be the same. 

Satya Nadella, CEO of Microsoft

Lastly, Nadella touches on future innovations in AI with mixed reality, where user interactions extend beyond language to gestures and gazes, and in AI with quantum computing, where simulations of natural phenomena can be emulated and quantum advancements can accelerate these processes. He envisions a future where such technology empowers every individual globally (actually Nadella expressed more on Microsoft’s vision of caring for the world and I appreciated it), offering personalized assistance in various aspects of life.

Nadella did a good job of expressing Microsoft’s vision on caring for our world. Microsoft will be “generating 100 percent of the energy they use in their datacenters, from zero-carbon sources by 2025.” He said that and next year is 2024. I hope they stay on track towards this goal.

Charles L. Bryant, Citizen of the World

The message concludes with a reference to a video featuring a Ukrainian developer’s experience with Copilot. This is also a lesson in the power of expressing the value of a product with story and emotion. Storyboard Copilot is coming too.

Why We Need to Bet on Agents Now

Let’s cut through the noise. Agents, these AI-driven digital workers, aren’t some sci-fi fantasy. They’re here, and they’re about to fundamentally change how you go about your day and how your business operates. Whether you’re building products, running marketing campaigns, or supporting operations or clients, understanding agents is no longer optional. It’s the key to getting and staying ahead.

Agents Are No Longer Theoretical

My prediction is that in the near future, agents will be indispensable. People won’t monitor their email. They won’t browse social media or use apps and websites as they do today. Their agents will do these tasks for them. These AI-driven workers will curate and deliver exactly what users need, without requiring them to use third-party user interfaces. We won’t have to log into Instagram or email. Our agent can stream email and content from other services through a single interface.

This will change marketing because marketers will have to learn how to attract agents to reach their human operators. Online stores will have to learn how to sell to agents. Agents make purchases on behalf of their human operators. Websites and apps won’t target humans but agents. If it can be done on a computer, agents will be able to do it. This includes phones. We need to rethink target users across our products. Our world will go through an epic paradigm shift.

Agents are still an emerging concept, and nothing is real or set in stone yet. However, early movers are already deploying agents. They use them to automate tasks, generate content, write code, and optimize decision-making. But here’s the kicker, most businesses don’t yet have agents tailored to their unique needs. This presents a massive opportunity. The potential applications are vast, and the market is wide open. If you get started today, we’re not just building agents; we’re writing the best practices for this transformation. By focusing on how to attract and build agents now, we’re positioning ourselves to thrive as the agent ecosystem grows.

This is our chance to step up as experts. Yes, we’re in uncharted territory, but that’s a good thing. I have made predictions here. However, no one really knows what’s coming. No one knows what to do to apply agents in industries. We’re not just building agents; we’re shaping the best practices that will define agents in our respective industries.

Why Early Adoption Matters

Being early comes with risks, but the opportunities and reward far outweigh them. By diving in now, we can shape the future of how agents are built, delivered, and operated. Early adoption means gaining:

  • Experience: Each agent we develop is a chance to learn from both success and failure. What works, what doesn’t, and how to pivot.
  • Credibility: As agents become mainstream, businesses will seek pioneers, those who’ve already proven their expertise and early results.
  • Market Advantage: Agents are self-improving. If we start soon, we will develop smarter and more capable agents sooner. Our systems will perform better compared to late adopters. Compounded learning will separate leaders from laggards. By diving in now, we gain an early entrance advantage in terms of experience and credibility. We also gain a head start in acquiring the precious data we need. This data is essential to feed our agents and improve their performance.

The Work Ahead

We must learn to build agents. We must also understand how to deliver and operate them as the best solution for specific use cases.

Delivering Agents

  • Planning: Understand the jobs to be done. Identify use cases, workflows, and challenges where agents can provide meaningful value.
  • Designing: Define clear objectives, user interactions, and system integration and interfaces for the agent.
  • Building: Train agents on the right data, using AI frameworks that allow flexibility and growth.
  • Testing and Iterating: Rigorously evaluate agent performance and refine based on real-world feedback.
  • Deploying: Introduce agents thoughtfully, ensuring seamless onboarding and integration with existing tools and workflows.
  • Releasing: Equip users with proper training and documentation to ensure successful adoption.

Operating Agents

  • Managing: Overseeing the agent’s functionality, ensuring it runs as expected, and addressing any operational issues.
  • Monitoring: Tracking real-time performance metrics, such as speed, accuracy, and user feedback, to ensure consistent quality.
  • Evaluating: Regularly reviewing the agent’s outcomes against its goals, identifying areas for improvement or additional training.
  • Improving: Iterating on the agent. This involves refining its prompts, templates, tools, and algorithms. We can update its RAG with new data. We can fine-tune it or retrain it with new data. We can also enhance its features to adapt to evolving needs.

Roadmap

Our roadmap to be successful with agents as a product focuses on both strategic insights and actionable steps.

  1. Understand the Jobs to Be Done: Not every task needs an agent, and replacing traditional digital solutions (e.g., websites or apps) requires clear benefits.
  2. Iterate Relentlessly: The first version of any agent won’t be perfect. It may often hallucinate and get things wrong. That’s fine. What matters is how quickly we learn and adapt.
  3. Collaborate Across Teams: Product, marketing, and support teams must all contribute. Everyone’s input is critical. The more perspectives we have, the better equipped we are to design and refine agents that excel.
  4. Measure and Optimize: Agents need monitoring and fine-tuning. Metrics like accuracy, speed, and user satisfaction will guide us.

Agents Improve Over Time

Let’s tackle a key truth, the first iteration of any agent will rarely deliver perfect results. Early versions might be clunky, prone to hallucinations, errors, or lacking the nuanced judgment needed for complex tasks. But that’s not a failure. It marks the beginning of an iterative process. This process allows agents to learn, adapt, and improve through data and feedback.

Unlike traditional solutions, which typically rely on fixed algorithms and human-driven updates, agents can operate dynamically. They evolve in real-time as they encounter new data and scenarios. This ability to self-optimize positions agents as uniquely suited for complex and evolving challenges where traditional solutions fall short.

  • Initial Challenges: In their infancy, agents might struggle with insufficient data, unclear objectives, or unexpected scenarios. These early hiccups can result in inconsistent performance or even outright errors.
  • Continuous Learning: With every iteration, agents refine their capabilities. New data helps them understand patterns better, adapt to edge cases, and make more accurate decisions. The more they’re used, the smarter they get.
  • Operator Involvement: Effective improvement requires skilled operators. We monitor agent performance. We analyze results and provide feedback and data. In doing so, we ensure agents evolve in ways that align with business goals.
  • Replacing Traditional Solutions: Over time, agents become faster. They become more accurate and better tuned to tasks. Eventually, they will outperform traditional solutions and humans. This transformation won’t happen overnight, but the incremental improvements lead to exponential results. Starting early helps us get through this journey faster than late adopters.

The goal isn’t perfection from day one. It’s about building a foundation that grows stronger and more capable with time.

A Vision for What’s Next

Agents will handle the tedious, time-consuming stuff, freeing us to focus on strategy, creativity, and big-picture thinking. Our clients see the results. Our stakeholders see the value. We get to lead the charge in one of the most exciting shifts in tech.

But this won’t happen by accident. It’s going to take the courage to move now with bold ideas and hard work. Its going to take a willingness to fail fast and learn faster. Let’s embrace the challenge and make it happen.

Let’s get to work! Do you want to talk about how to start or improve your agentic ops journey, I’m here.

AgenticOps: Transform Your Business with Agent Enhanced Teams

Welcome to 2025 and the year of AgenticOps.

Can your business operating systems think for themselves? Can they adapt in real time? Do they make decisions that perfectly align with your strategic goals? Imagine a system that is seamlessly integrated with your workflows. It not only reduces manual overhead but actively amplifies your team’s effectiveness. This vision is no longer aspirational—it’s here. Welcome to AgenticOps, the new frontier in how organizations operate, collaborate, and deliver value.

What is AgenticOps?

AgenticOps (Agentic Operations) isn’t your typical automation or AI solution. It’s a comprehensive, structured framework where autonomous agents collaborate with human operators to achieve shared strategic and tactical goals. Picture a network of hyper-specialized agents. These could include planner agents, research agents, developer agents, and more. They work tirelessly behind the scenes to streamline processes. They optimize results and keep priorities aligned.

These agents are not mere tools. They are intelligent systems designed to learn, adapt, and execute tasks as an extension of your team. These systems enable better alignment and faster decisions. They also promote proactive problem-solving and seamless orchestration of complex workflows.

The Core Principles of AgenticOps

AgenticOps is built on three foundational pillars:

1. Autonomy

Agents operate independently, performing tasks without constant human oversight. But, they rely on human feedback and approval for critical decision points. These agents autonomously monitor, analyze, and act on data, ensuring their actions align with your organization’s strategic objectives.

2. Collaboration

Agents are interconnected, working as a cohesive system. They share insights, offer feedback, and coordinate activities across workflows. This eliminates silos, streamlines communication, and ensures seamless collaboration between agents and human operators.

3. Accountability

Every agent’s action is transparent, traceable, and purpose-driven, aligning with predefined objectives. Accountability fosters trust and ensures operational integrity, empowering teams to rely on agents while maintaining control.

The Value of AgenticOps

The transformative potential of AgenticOps lies in the value it delivers to organizations:

1. Strategic Alignment

By embedding strategic goals into every operation, AgenticOps ensures resources are directed toward the most impactful tasks. This integration drives measurable business outcomes.

2. Efficiency at Scale

Agents handle repetitive, high-volume tasks with precision, freeing human operators to focus on creative, innovative, and high-priority initiatives.

3. Real-Time Adaptability

In dynamic environments, agents adapt instantly—reallocating resources, recalibrating priorities, and maintaining operational continuity in response to market demands.

4. Enhanced Visibility

Agents continuously monitor and report on operational performance, providing unparalleled insights into bottlenecks, inefficiencies, and opportunities for improvement.

The Specialized Roles of Agents in AgenticOps

AgenticOps systems are powered by specialized agents designed to handle distinct responsibilities:

  • Strategy Agents: Define and maintain the strategic objectives that guide all operations.
  • Plan Agents: Develop and update plans, ensuring timelines and milestones align with goals.
  • Research Agents: Conduct user research, market analysis, and feasibility studies to provide actionable insights.
  • Tech Agents: Manage the heavy lifting in development, design, QA, and release engineering.
  • Manager Agents: Oversee workflows, align tasks with strategic goals, and maintain accountability.

Together, these agents form a synergistic system that empowers businesses to operate with unprecedented precision and agility.

A Real-World Example of AgenticOps in Action

Consider a product team managing a portfolio of client projects. In traditional setups, tracking progress, aligning with strategic goals, and adjusting priorities require multiple tools, meetings, and manual interventions.

With AgenticOps, Manager Agents dynamically analyze work in progress, monitor KPIs, and provide actionable insights. If a bottleneck arises, agents flag the issue, recommend solutions, and execute corrective actions autonomously. This proactive approach keeps revenue targets on track, eliminates delays, and ensures client satisfaction.

How to Adopt AgenticOps

Transitioning to AgenticOps requires a strategic, phased approach:

  1. Start Small: Identify high-impact areas where agents can deliver immediate value, such as planning or research.
  2. Integrate Incrementally: Introduce agents gradually, ensuring they complement existing workflows.
  3. Empower Teams: Provide teams with the training and tools needed to collaborate effectively with agents.
  4. Measure Success: Use metrics to track the impact of agents, iterating to refine their contributions.

The Future of Work is AgenticOps

AgenticOps is more than a technological advancement. It’s a thought process and paradigm shift that will force businesses to evolve. Otherwise, they lose against businesses that make the shift. Its not a new idea, its our framework to take advantage of the rapid advancements in AI. By embedding intelligence into operations, businesses become adaptive, resilient, and capable of thriving in fast-paced environments.

This isn’t about replacing humans. It’s about empowering them—reducing cognitive load and enabling them to focus on what truly matters. The question is no longer, “What can your team do for your business?” It’s now, “What can your agent-augmented team achieve for your business?”

Have you begun the shit to AI driven operations? How are you succeeding with something like AgenticOps? Let us know your thoughts and join the conversation.

2025: The Year AI Transforms Work and Industry at Scale

Happy New Year! Here are my bet’s for 2025.

The rapid evolution of artificial intelligence is reshaping industries and redefining how we work. In 2025, I predict that several transformative trends will reshape the landscape of software development, industry specialization, and workforce dynamics. Below, we explore these predictions, provide additional insights, and examine the opportunities and risks that come with these changes.

1. Accelerating Improvements in LLMs

Large Language Models (LLMs) will continue to push the boundaries of what AI can do. With advancements in transformer, SSM, and other architectures, fine-tuning, and multimodal learning, LLMs will deliver faster, more accurate, and contextually rich results. We may even see an new architecture that pushes LLMs ahead even faster.

Opportunities

  • Enhanced productivity and creativity in tasks like content creation, research, and customer interaction.
  • Multimodal capabilities enabling seamless integration of text, images, and even video into workflows.

Challenges

  • Ethical concerns around misuse, bias, and misinformation.
  • Increased compute requirements may exacerbate energy consumption concerns.

2. Smaller Models, Big Potential

Compact LLMs leveraging distillation, pruning, and quantization will achieve near-parity with today’s largest models. This shift will democratize AI, making it accessible for edge devices, IoT applications, and industries with limited compute resources.

Opportunities

  • Cost-effective AI solutions for SMBs.
  • Expanding AI’s footprint into rural and underserved areas via low-power devices.

Challenges

  • Balancing efficiency with accuracy in critical applications like healthcare diagnostics or autonomous vehicles.

3. Industry-Specific AI Takes Center Stage

The rise of verticalized AI solutions tailored to specific industries, such as marketing, healthcare, finance, and legal, will dominate the market. These solutions will provide unparalleled domain expertise, driving faster ROI for businesses.

Opportunities

  • Precision and relevance in solving domain-specific challenges.
  • Increased trust in AI adoption as models demonstrate real-world impact.

Challenges

  • Heavy reliance on proprietary data could lead to monopolistic behavior or widen the gap between industry leaders and smaller players.

4. AI-Driven Development as the Norm

AI coding assistants like GitHub Copilot, Cursor, and Aider are already changing the way developers work. In 2025, AI will be fully integrated into development ecosystems, assisting with ideation, debugging, and even deployment.

Opportunities

  • Streamlined development cycles, reducing time to market.
  • Better accessibility for non-traditional developers, diversifying the talent pool.

Challenges

  • Over-reliance on AI could erode foundational coding skills.
  • Potential loss of creativity in problem-solving as AI-driven patterns become standardized.

5. Coding Becomes a Commodity

As AI handles the technical details of coding, the value of knowing how to code will diminish. Instead, the ability to guide AI assistants, define clear objectives, and solve high-level problems with code will become paramount. The experience to know when code is good or bad becomes more important than the just the ability to code.

Opportunities

  • Broader inclusion of non-technical professionals in tech projects.
  • Emergence of new roles focused on prompt engineering, strategy, and oversight.

Challenges

  • A potential skills gap for current developers who fail to adapt.
  • Risk of job displacement without sufficient upskilling initiatives.

6. Shippable AI-Driven Products with Minimal Oversight

In 2025, performant coding agents capable of turning product requirements into deployable solutions will emerge. These agents will handle well-defined scopes but may still require human oversight for complex or high-stakes applications.

Opportunities

  • Faster MVP development for startups.
  • Automated maintenance of legacy systems, freeing up human resources for innovation.

Challenges

  • Ensuring quality control and accountability in AI-generated products.
  • Difficulty in generalizing complex, nuanced requirements.

7. OpenAI Dominates but Faces Competition

OpenAI will maintain a stronghold on the market, but competition from Google, Meta, Anthropic, Cohere, and open-source ecosystems will heat up.

Opportunities

  • Diverse options for businesses to choose from, fostering innovation and reducing costs.
  • Strengthening open-source movements that promote transparency and collaboration.

Challenges

  • Risk of market fragmentation, making it harder for businesses to standardize solutions.
  • Proprietary dominance could limit interoperability.

8. AI Agencies Replace Traditional Agencies

Small AI agencies will rise to prominence, offering specialized services in automation, data modeling, and AI-driven marketing and development. These agencies will cater to SMBs, replacing traditional creative and technical firms.

Opportunities

  • Affordable, tailored AI solutions for local markets.
  • Innovation in personalized customer experiences and hyper-local strategies.

Challenges

  • Ethical dilemmas in hyper-targeted advertising.
  • Limited oversight in emerging markets where regulation lags behind.

9. Data Becomes the New Gold for Real This Time

Businesses will finally fully realize the value of their proprietary data, using it to train domain-specific models. Data-rich companies will dominate their industries by leveraging AI in unique and powerful ways.

Opportunities

  • Competitive differentiation through unique datasets.
  • Greater investment in data quality, security, and governance.

Challenges

  • Risk of data monopolies exacerbating inequality.
  • Increased cybersecurity threats targeting proprietary datasets.

Navigating Risks and Building for the Future

While the predictions for 2025 are exciting, they also come with challenges that require proactive measures:

  • Upskilling the Workforce: Governments, businesses, and educational institutions must collaborate to prepare the workforce for AI-driven roles.
  • Regulating Ethically: Establishing global standards for AI use will be crucial to avoid misuse and ensure equitable benefits.
  • Driving Sustainability: Advancements in AI must prioritize energy efficiency and sustainable practices.

As we move into 2025, businesses that embrace these changes while navigating risks will unlock unprecedented opportunities. The future of work and industry is brighter, more efficient, and deeply collaborative—driven by AI.

Are you ready to harness the power of AI for your business? Let’s talk about it and get your business ready for our AI future.

A Future Vision of Software Development

From Coders to System Operators

As artificial intelligence (AI) continues reshaping industries, the role of software development is undergoing a profound transformation. Writing code is becoming less about crafting individual lines of code and more about designing systems of services that deliver business value. Development is shifting from writing code to creative problem-solving and systematic orchestration of interconnected services.

The End of Coding as We Know It

Code generation has become increasingly automated. Modern AI tools can write boilerplate code, generate tests, and even create entire applications from high-level specifications. As this trend accelerates, human developers will move beyond writing routine code to defining the architecture and interactions of complex systems and services.

Rather than focusing on syntax or implementation details, the next generation of developers will manage systems holistically, designing services, orchestrating workflows, and ensuring that all components deliver measurable and scalable user, client, and business value.

The Rise of the System Operator

In this emerging paradigm, the role of the System Operator comes into focus. A System Operator oversees a network of AI-driven assistants and specialized agents, ensuring the system delivers maximum value through continuous refinement and coordination.

Key Responsibilities of the System Operator:

  1. Define Value Streams: Identify business goals, define value metrics, and ensure the system workflow aligns with strategic objectives.
  2. Design System Architectures: Structure interconnected services that collaborate to provide seamless functionality.
  3. Manage AI Agents: Lead AI-powered assistants specializing in tasks like strategy, planning, research, design, development, marketing, hosting, and client support.
  4. Optimize System Operations: Continuously monitor and adjust services for efficiency, reliability, and scalability.
  5. Deliver Business Outcomes: Ensure that every aspect of the system contributes directly to business success.

AI-Augmented Teams: A New Kind of Collaboration

Traditional product development teams will evolve into AI-Augmented Teams, where every team member works alongside AI-driven agents. These agents will handle specialized tasks such as market analysis, system design, and performance optimization. The System Operator will orchestrate the work of these agents to create a seamless, value-driven product development process.

Core Roles in an AI-Augmented Team:

  • Strategist: Guides the product’s vision and sets business goals.
  • Planner: Manages delivery timelines, budgets, and project milestones.
  • Researcher & Analyst: Conducts in-depth user, customer, market, technical, and competitive analyses.
  • Architect & Designer: Defines system architecture and creates intuitive user interfaces.
  • Developer & DevOps Tech: Implements features and ensures smooth deployment pipelines.
  • Marketer & Client Success Tech: Drives user adoption, engagement, and retention.
  • Billing & Hosting Tech: Manages infrastructure, costs, and financial tracking.

System Operator: A New Job Description

A System Operator is like an Uber driver for business systems. Product development becomes a part of the gig economy.

Operators need expertise in one or more of the system roles with agents augmenting their experience gaps in other roles. System Operators can be independent contractors or salaried employees.

Title: System Operator – AI-Augmented Development Team

Objective: To manage and orchestrate AI-powered agents, ensuring the seamless delivery of software systems and services that maximize business value.

Responsibilities:

  • Collaborate with other system operators and AI-driven assistants to systematically deliver and maintain system services.
  • Define work item scope, schedule, budget, and value-driven metrics.
  • Oversee service performance, ensuring adaptability, scalability, and reliability.
  • Lead AI assistants in tasks such as data analysis, technical research, and design creation.
  • Ensure alignment with client and agency objectives through continuous feedback and system improvements.

Skills and Qualifications:

  • Expertise in system architecture and service-oriented strategy, planning, and design.
  • Strong understanding of AI tools, agents, and automation frameworks.
  • Ability to manage cross-functional teams, both human and AI-powered.
  • Analytical mindset with a focus on continuous system optimization.

Conclusion: Embracing the Future of Development

The role of developers is rapidly evolving into something much broader, more strategic, and less focused on boilerplate coding. System Operators will lead the charge, leveraging AI-powered agents to transform ideas into scalable, value-driven solutions. As we move toward this new reality, development teams must embrace the change, shifting from code writers to orchestrators of complex service ecosystems that redefine what it means to build software in the AI era.