Agents Are The New Mobile Moment

The hype is loud, but the results are quiet—and that’s the paradox we set out to crack. We make the case that real ROI shows up when AI stops answering and starts acting. By drawing a sharp line between chatbots and autonomous agents, we walk through the four capabilities that matter—memory, reasoning, deep integration, and execution—and show how they compound into speed, accuracy, and measurable outcomes across the business.

TLDR / At A Glance

• the platform shift from chatbots to agents
• the four capabilities of agents: memory, reasoning, integration, execution
• the agent loop: perceive, think, act, learn
• the move from tool trials to agent hives and redesigned work
• measured impact on satisfaction, cost, and cycle time
• new models: Agent-as-a-Service, autonomous processes, embedded intelligence
• first-mover advantages in data, CX, efficiency, and talent
• a practical path to start with one outcome and iterate

We break down the agent loop in plain language: how Perceive goes beyond prompts to a live map of your data, how Think plans steps and tools, how Act connects to your CRM, ERP, and comms stack, and how Learn closes the loop so performance improves over time. Then we zoom out to the bigger arc: today’s iPhone moment sets the stage for an App Store-style wave where agent hives coordinate entire workflows—from trend scanning to content creation to orchestration—while humans step up to review and risk-manage.

You’ll hear concrete proof points and emerging playbooks. We highlight customer service where autonomous resolution now handles the bulk of routine issues, the 60–90% cycle-time cuts when handoffs disappear, and the cost savings that fund faster innovation. We explore new business models—Agent-as-a-Service, autonomous processes in logistics and real estate, and embedded intelligence in industrial gear and software—alongside the competitive dynamics that reward early movers with better data, stronger partnerships, and top talent.

If you’re choosing where to begin, we share a simple path: pick one high-volume, rules-heavy workflow, expose the right data, set guardrails for autonomy, and instrument the learn step from day one. The window for first-mover advantage is shrinking by the month. Subscribe, share this with a leader who needs it, and leave a review with the one workflow you’d hand to an agent tomorrow.

Want some free book chapters?  Then go here How to build an agent - Kieran Gilmurray

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Chapters

• 0:00 The Agentic Economy Emerges
• 1:44 Agents Versus Chatbots
• 3:20 Four Capabilities That Matter
• 3:55 The Agent Loop Explained
• 6:00 From iPhone Moment To Agent Hives
• 8:10 Proof Points And Measured Impact
• 9:32 New Business Models Open Up
• 12:06 Lessons From Past Platform Shifts
• 13:09 Compounding Advantages Of Moving First
• 15:03 Closing Challenge And Call To Lead

Transcript

SPEAKER_00: 0:00

Chapter 1. The Platform Shift Creating New Market Leaders The business world is experiencing its most significant platform shift since the mobile telephone revolution disrupted entire industries over 15 years ago. Just as companies that embraced mobile first strategies captured disproportionate market share, while legacy players scrambled to catch up, we're now witnessing the emergence of the agentic economy. Almost every company, 80%, has incorporated generative AI somewhere into its technology stack. And nearly every one of those companies, 80%, says it hasn't impacted their bottom line. McKinse calls this the Gen AI paradox. It is just the usual story of new technology. Everyone rushes to adopt it because they're afraid of being left behind, but they don't know how to monetize it. The way out of the paradox is autonomous agents, not just chatbots duct taped onto a help page, but actual systems embedded in the organizational bloodstream, allowed to do things on their own. That sounds cool in theory, but in practice, it requires the one thing corporations struggle with, thinking clearly about their goals, processes, structures, and incentives before investing money in technology. So, we're at another inflection point. Either companies figure out how to deploy agents in a way that's as transformative as mobile first was for Apple and Google, or they will end up as the next BlackBerry and Nokia, remembered mostly in business school case studies and where they now slides. AI agents vs chatbots, proactive vs. reactive systems. Understanding the distinction between AI agents and chatbots is crucial if your goal is to achieve digital transformation in business operations. Chatbots respond to specific queries, and if they are integrated with an LLM-based API, they can then generate unique and relevant content in response to prompts. AI agents operate with autonomy, memory, and the ability to execute complex workflows across multiple computer systems. In 2025, an agent can communicate with the customer, confirm that their payment was successful, verify for potential fraud, resolve a customer's billing issue, reprocess a shipment, and then log all of this information in the CRM. This shift from reactive to proactive, i.e., from answering to acting, it's exactly where competitive advantage now lies. The leap from chatbots to agents comes from four capabilities. Memory systems. Unlike the amnesiac chatbot, an agent remembers the last conversation, the last action, and the last outcome, building a stateful representation of the user interaction or process workflow. Reasoning capabilities Agents can break down complex business challenges into component parts, examine the relationships between those parts, and then create and execute effective strategies to address them. Integration power. Like an API cyborg, an agent embeds itself as a nervous system across your company software, allowing it to coordinate actions between previously siloed corporate organs such as CRM, ERP, and email. Autonomous execution. Perhaps most critically, agents can take action without constant human prompting. This will eventually eliminate the need for many of the middle management's current tasks, which is a big business advantage. The agentic loop. Agents operate on a four-stage cycle, called the agent loop. This loop may also be referred to by other names, such as the Perceive Reason Act Cycle, or the Thought, Action, Observation Cycle. Perceive. The agent takes in data from its environments. Not just a user's text query, but a constant stream of information. It could be a real-time feed of market data, usage logs from your CRM, new support tickets, or an urgent email from a key client. Through all this input, the agent builds a high-res map of its assigned domain. Think. The agent analyzes the data it has perceived, breaks down its high-level goal into executable steps, and decides which tools it needs to use. For instance, an agent would ask to schedule a meeting with the sales team to discuss the Q3 forecast. They would first find a mutual opening on everyone's calendar, then search the shared drive for the relevant forecast document, and finally, schedule the meeting with the updated Q3 forecast document attached. Act. The agent executes the plan by connecting to other systems via APIs. For example, by logging into a CRM, drafting an email, booking a meeting, etc. Here, you have the choice of having a human in the loop to validate, and check the agent's actions or letting it do these things autonomously. Learn. The agent observes the outcome of its actions and integrates this feedback into its memory, refining its strategies over time. For example, it can learn which outreach messages are most effective and which customer behavior patterns are the true predictors of churn, among other things. The growth wave. If 2024 to 2025 is the iPhone moment, then the period from 2026 to 2030 will be akin to the app stored explosion. The real action starts not when the tool exists, but when a million experiments begin to stack on top of it. The first wave of agent adoption is mostly about novelty and tinkering. You give people a tool, they try it, they collect data. The data feeds back into the system, the system improves, and then that improvement feeds back into the users, who generate more ambitious use cases, which in turn feed more data back into the system, and so forth. By 2028, it won't just be your agent books your meetings or your agent writes SEO content. A loan agent today might already draft a blog post. The Agent Hives of 2028 will autonomously manage the entire action sequence. One agent continuously monitors search trends and news for new and emerging topics. A second agent, with a different personality tuned for virality, drafts the first written piece. A third generates a suite of complementary visual and social assets. A fourth acts like the project manager who makes sure the other three executives and then presents the final result to a designated human. As Deloitte has commented, the real value of AI is found in redesigning work to integrate humans and machines within entire workflows. This is where the real value lies. Not in isolated, prompted toy tasks, but in entire workflows stitched together, with humans increasingly acting more as reviewers and orchestrators. If this model holds, the companies that built their muscles early, in 2025 to 2026, when everyone else was still deciding whether agents were a fad, will dominate ahead. They'll already have the datasets and institutional know-how to iteratively refine their agentic infrastructure, but you won't if you haven't even started. Companies using agents, seeing dramatic improvements. Recent research by McKinsey reveals that companies successfully deploying AI agents are seeing substantial improvements. Customer satisfaction enhancements. McKinse reports that up to 80% of common service incidents can be resolved autonomously. Operational cost reduction. Early case studies show that AI agents can streamline repetitive, error prone tasks and improve overall productivity. In one study, generative AI improved issue resolution by 14% per hour for customer service agents, thereby reducing handling times and enabling them to focus on more complex cases that truly required their time and attention. Process acceleration. In scenarios where workflows have been fully reimagined with agent autonomy, McKinsey estimates that end-to-end process times can drop by 60 to 90%, since autonomous agents eliminate delays from human handoffs and execute activities in parallel. These results are not just theoretical, they are most visible in organizations that are moving beyond pilot projects toward the deeper integration of agentic AI into core operations. The agents economy, new business models, and revenue streams opening up. Sometimes, technologies don't just make existing things faster, they change what things are. Cars weren't just faster horses, they were the start of suburbs, road trips, drive-throughs, and traffic jams. Smartphones weren't just smaller computers, they were Uber, Tinder, food delivery apps, PayPal, and the dopamine economy of Infinite Scroll. Agent as a Service models. Build an agent, sell it. OpenAI now enables companies like Stripe and Box to create custom business agents and monetize them as APIs. Consider, a bank's internal risk scoring system is transformed into a product that outsiders can rent, generating a new income stream for the bank. Autonomous business processes. Agents can handle complete workflows independently. Real estate agents, the digital kind, can identify opportunities, conduct market analysis, and even manage early negotiations. For instance, DHL runs AI agents that optimize routes, manage warehouses, forecast demand, and reduce travel distance. Embedded intelligence revenue. Products gain autonomous capabilities. Agricultural equipment scheduled its own maintenance based on usage patterns and weather forecasts. Companies like Siemens and GE use embedded AI agents to monitor turbines, jet engines, and factory equipment. Software applications adapt their interfaces and functionality to individual users over time, enabling companies to react with more agility, intelligence, and impact than their competition. Dynamic pricing and personalization. Agents can personalize beyond recommendations. E-commerce platforms adjust pricing, product placement, and promotional offers in real time based on individual customer behavior, inventory levels, and competitive positioning. There is huge economic potential. Industry analysts project the AI agent market will reach$50 billion by 2027, but this figure likely underestimates the broader economic transformation as agents become embedded throughout business ecosystems. Competitive risk. History provides clear precedent for what happens during platform transitions. When mobile computing emerged, companies faced a critical choice to embrace the new platform early and risk short-term costs and complexity, or wait for the technology to mature and risk being displaced competitively. The winners were decisive. Apple didn't wait for mobile technology to be perfect before launching the iPhone. They bet on the platform's trajectory and captured disproportionate value as the ecosystem developed. Similarly, companies like Uber, Airbnb, and Instagram built their entire business models around mobile capabilities that established players dismissed as immature or niche. Today's AI agent adoption follows similar patterns. Early movers are not waiting for perfect technology. They are building competitive advantages while the competitors debate implementation timelines. The risks of waiting compound daily. First, mover advantage. Companies that deploy agents now are building institutional knowledge, refining processes, and developing agent-optimized workflows that will be difficult for competitors to replicate. They're also establishing relationships with key technology partners and securing top AI talents before the market becomes saturated. Data Network Effects Agents depend on the quality and accessibility of enterprise data. Organizations that begin integrating agents immediately start reaping the benefits of data network effects. As agents process more information, they become more effective, creating a self-reinforcing competitive advantage. Customer experience modes. Companies that utilize agents to deliver superior customer experiences are elevating customer expectations across the industry. Competitors offering traditional service models appear increasingly outdated by comparison, making it progressively harder to win new customers or retain existing ones. Operational efficiency gaps. Organizations that achieve cost reductions through agent deployment can reinvest those savings in further innovation, marketing, or competitive pricing strategies, thus creating compounding advantages over competitors who still operate with manual processes. Talent and partnership access. The ecosystem of agent-capable developers, consultants, and technology partners is still in its early stages of development. Early movers have preferential access to the best resources, while later adopters face increasingly competitive talent markets and longer implementation queues. Concluding thoughts. The mobile platform shift created trillion dollar companies and obsoleted entire business models within a decade. The agent platform shift is following a similar trajectory, but with compressed timelines and amplified impact. The window for establishing first mover advantage is now measured in months, not years, and shrinking. The platform shift is underway. Will your company be among the agents of change or among the casualties of transformation?