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Insights Navigating the Agent Revolution: Prepare Your Crew for AI’s Next Wave
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Navigating the Agent Revolution: Prepare Your Crew for AI’s Next Wave

Last week, I ran into the crew I used to race with as the sailing season just kicked off here in Wisconsin. One thing becomes clear on a sailboat: the captain may set the strategy, but winning the race depends on every crew member knowing their role and executing at the right time. Teamwork and timing are everything, whether it’s one person trimming the sails while another balances the boat, or someone calling out wind shifts. It occurred to me that this is a perfect analogy for what’s happening in tech today. We’re on the cusp of an “agent revolution” in AI – a surge of intelligent agents working together – and success will depend on how well each part of our “crew” (both humans and AI agents) can collaborate.

AI agents are no longer science fiction – they’re here and growing fast. I’ve been using AI agents regularly since the beginning of the year in my day-to-day work. These agents can autonomously handle tasks like research, summarizing reports, or even generating images. It’s exciting and a bit jolting at the same time. I find myself switching between different tools – asking questions in Teams or ChatGPT, using a GitHub Copilot for code, refining content in Word, then toiling away in PowerPoint, and jumping back to generate images (for this blog). Does it feel disjointed? Absolutely. Each tool is like a talented crew member, but they aren’t always in sync with one another. If I feel this jolt as I switch between AI-powered activities, I can only imagine how end users feel bouncing between siloed intelligent features. The potential of these agents is magnificent (brilliant!) – but right now it’s as if each crew member is doing their own thing and I’m stuck barking orders to figure it all out. The good news is that a more unified, coordinated approach is on the horizon.

Let’s talk about how to get ready for it.

The Agent Revolution Is Here

Not long ago, “generative AI” meant typing a prompt into a chatbot or getting code suggestions or summarizing document content. Now we’re moving into a new era: autonomous AI agents that don’t just respond to prompts – they take initiative to help us achieve goals. Instead of a passive tool, an AI agent can plan steps, call APIs or other services, and collaborate with other agents to handle complex tasks. This is the agentic AI movement, and it’s gaining momentum. Gartner predicts that by the end of 2025 AI agents will be involved in 50% of business decisions, up from just 5% in 2021. In other words, these “digital crew members” are quickly becoming central to how work gets done.

Why the excitement? Because agents promise to automate and orchestrate the busywork of our digital lives. Imagine an AI agent that can not only draft an email but also schedule the meeting it’s about (good luck with my calendar), update CRM with the meeting notes, and the coordinate with another agent that handles the team’s task list. It’s like having a tireless crew that anticipates what needs to happen next. Tech companies are investing heavily here – in fact, two new open standards called Agent2Agent (A2A) and Model Context Protocol (MCP) were recently to enable AI agents to talk to each other and coordinate actions across different platforms. The industry is preparing for a future where agents from different vendors or teams seamlessly collaborate.

Why Companies Hesitate

Why isn’t every company on board today?  I’ve heard a lot of reasons why organizations pump the brakes on adopting AI agents. First, there’s a skills gap. Building and integrating AI (especially autonomous agents) requires skills that many teams don’t yet have. The demand for AI/ML talent far outstrips supply, and companies worry they lack people who “speak AI” fluently. Training your existing crew takes time, and hiring experts is competitive and costly. It’s completely understandable that some leaders look at their team and think, “We’re not ready for this voyage yet.”

Second, there’s a lack of trust. Handing over tasks to an autonomous agent can feel risky. Will it make good decisions? How do we ensure it won’t go off course? End-user acceptance is a real concern – people need to trust the AI’s outputs and not fear that the “AI autopilot” will crash the ship. Building that trust means addressing quality, reliability and head-on. Early adopters have faced some wobbles with AI making mistakes or unpredictable choices, so skepticism is natural. As one report put it, user acceptance and trust must be earned by addressing concerns (even things like job displacement) and proving the AI’s value.

Finally, many organizations are still digging out of the tech-debt hole. They have legacy systems, spaghetti code, and outdated infrastructure (still!?!). Introducing advanced AI into a brittle old system can feel like trying to mount a modern sail on a boat full of holes – you’ll rip something. These companies sensibly focus on fixing the ship (refactoring, modernizing, cleaning up data) before adding new AI capabilities. Technical debt doesn’t just slow down new development; it can also undermine trust in AI results if the underlying data and systems aren’t reliable. In short, hesitation isn’t futile; it’s often a sign of wisdom and prudence. It’s okay to say “not yet” – but there are still things you can do today to be ready when the time is right.

Prepping for the Surge

The agent revolution is coming fast, like a gust of wind shifting in your favor. Even if you’re not ready to fully embrace AI agents in your application today, you can prepare your ship and crew so you don’t get left behind. Here are some actionable steps to get ready for the surge:

  1. Upskill Your Crew: Start investing in AI knowledge across your team. You don’t need everyone to be a data scientist, but ensure your developers, architects, and product folks understand the basics of modern AI capabilities. A well-trained crew handles new tech like a seasoned sailor handles sudden winds.
  2. Build Small Trust Pilots: It’s hard to trust what you don’t know. So, run small pilot projects or proofs-of-concept with AI agents in non-critical areas. Use human-in-the-loop when you start in order to garner trust. Pick a low-risk, high-learning scenario. This lets your organization see an agent in action and learn its quirks.
  3. Tackle Tech Debt & Prepare Your Data: Use this time to get your house in order. Clean up legacy code (with Copilot), refactor that one module everyone’s afraid to touch, and improve your data quality. Agents thrive on good data and robust APIs. If your application will one day work with AI agents, it needs to provide clean “hooks” and reliable information. Consider it like scraping off barnacles and reinforcing the hull of your ship now, so that when you do add an AI-powered engine, the boat can handle the speed.
  4. Instrument and Observe (Telemetry Readiness): I’ll dive deeper into this in my next post (maybe), but you should start enhancing your application’s observability now. In an AI-driven ecosystem, knowing what’s happening in your app (user actions, errors, performance bottlenecks) is gold. Put the right telemetry in place – user event logs, metrics, traceability of key actions. Not only will this help you run your system today, but it will set the stage for AI agents to utilize that context in the future. A ship with good instrumentation (compass, depth finder, an eye on the sky) is far safer and more effective than one sailing blind. So add those “sensors” to your app.
  5. Think in ‘Tasks and Goals’: Begin shifting your mindset (and your application design) from just features to user goals and tasks. Agents operate by understanding goals and taking actions to fulfill them. Ask yourself: what are the top things our users are trying to accomplish, and what steps do they typically have to take? Where do they get stuck or slowed down? By identifying these, you’ll see where an AI agent might slot in to help. Even if you won’t implement it yet, this exercise prepares you to design agent interactions that are truly user-centric when the time comes.

Explore Your Own Journey

Reading about all this is one thing, but experiencing it is another. I invite you to explore your own journey with AI agents – both as a user and as a builder – so you can personally grasp the possibilities and challenges. If you haven’t already, try using an AI agent or two in your daily routine. This could be as simple as using a smart email triage assistant, an AI scheduling tool, or a coding Copilot in your IDE. Pay attention to when it delights you and when it frustrates you. Do you feel that jolt when switching contexts or tools? Note those moments: they are exactly the pain points the next generation of agent solutions aims to smooth out.

Reflect on your workflows and your product’s user journeys. Where are the repetitive tasks that drain time? Which processes require hopping between multiple systems or copying data from here to there? Those are strong candidates for agent augmentation. For example, if you’re a business leader, maybe your sales team spends hours collating weekly reports – an AI agent could potentially handle that collation and surface key insights, freeing your team for higher-value conversations. If you’re a developer, think about that gnarly bug hunt last week – would having an AI buddy that could sift through logs and highlight anomalies have helped?

Consider running a mini-experiment: take a small problem you have and see if an AI agent (or even a simple automation script) could tackle it. This isn’t about implementing enterprise-grade AI overnight; it’s about getting your feet wet. Try out an open-source agent framework or even a no-code automation tool that strings together a couple of services (which is a primitive form of what A2A will generalize). The goal is to learn by doing. Share these experiences with your team – spark the conversation about what worked and what didn’t. This exploration will build intuition about where agents could fit and where you need more prep. Plus, it often ignites excitement; nothing builds buy-in like people seeing a new tool actually solve a pain point in front of their eyes.

Finally, imagine and discuss openly: what would a fully agent-enabled version of your application look like in a few years? How would the user experience change? How would your internal operations change? By visualizing the destination, you can better plan the journey. It’s like plotting a course on a map before setting sail – you might still face unexpected weather, but at least you know where you’re headed. And don’t be shy about voicing your questions or concerns. We are all figuring this out together, much like a crew finding its rhythm.

(If you have experiences with AI agents or questions about preparing for this shift, I’d love to hear your thoughts – let’s learn from each other’s journeys.)

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