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Research
For the past few years, AI has made one of the largest technological impacts on almost all commercial industries. Today, most large corporations have spent considerable amounts of money into either research or implementing it in their workforce. In fact, it has completely transformed how we work. We ask, they answer; we prompt, they generate. This model has been a powerful force for productivity, but it has a built-in limitation: it's passive. It waits for you. Now, a fundamental shift is underway: agents are transforming proactively from copilots to digital coworkers.
This is agentic AI: goal-directed systems that don't just answer questions, but plan, take action, and "drive" software on their own to achieve end-to-end outcomes. They can function as persistent, autonomous members of the team, capable of managing complex, multi-step processes over time. Analyst coverage in 2025 has placed AI agents squarely on the executive agenda. Gartner’s latest AI Hype Cycle, for instance, names them among the fastest‑advancing innovations, signaling that CIOs and boards are paying close attention. (Gartner)
So, what truly separates a "copilot" from an "agent"? A copilot is a tool you wield; an agent is a teammate you delegate to.
McKinsey offers a clear definition for 2025: it’s a system that can understand a high‑level goal, break it into logical sub‑tasks, interact with humans and software, and adapt its approach with minimal oversight; all enabled by a convergence of LLMs, planning, memory, and integrations. (McKinsey & Company)
In practical terms, think of it as a software teammate. A good teammate doesn't just wait for explicit, step‑by‑step instructions. They anticipate needs, handle routine work proactively, and report back on outcomes, not on every tiny action. Let’s look deeper and put it into context:
A Copilot: You ask it to "draft an email to our logistics partner about the shipping delay for order #12345." It gives you a perfect draft, and you send it.
An Agent: You give it the goal: "Ensure our premium customers are proactively managed during shipping delays." The agent then:
The agent didn't just write; it monitored, decided, and acted across multiple systems to achieve a business goal.
GenAI isn’t brand-new technology; it’s been experimented with for a long time. It has, however, become more widely known for public use with OpenAI being released in 2022, and since then has exponentially grown for both business and consumer use through agentic AI. So what’s changed recently that’s allowed it to become more functionally widespread?
Agents Can Use Computers Like We Do
For decades, automation was blocked by the "last mile": legacy systems, internal dashboards, and supplier portals that will never have a modern API. Now, agents can operate software just like a person. OpenAI’s Operator and Google’s Gemini 2.5 Computer Use (both 2025) let agents autonomously click, type, and navigate webpages -unlocking the long tail of processes that were previously un‑automatable. (OpenAI)
More Powerful "Agent‑Class" Models
The underlying engines are stronger and more reliable for long‑running, multi‑step work. Anthropic’s Claude Sonnet 4.5 (2025) is positioned for building complex agents and for robust computer use, with gains in reasoning and sustained tool use. (Anthropic)
The Developer Ecosystem Has Arrived
The shift from isolated bots to a scalable workforce is enabled by new, first‑party tooling from major providers. OpenAI’s AgentKit (Oct 2025) formalizes building and deploying agents; AWS’s Bedrock AgentCore became generally available in Oct 2025 to build/run agents at scale; and Google’s Gemini Enterprise positions itself as a “front door” to agents at work. (OpenAI)
So where are these Agents really making an impact? Businesses have been using AI for years now, but it’s only just starting to be integrated into the workforce in a meaningful way. Up until recently, AI has appeared most prominently as chatbot assistants. As technology progresses, people are starting to rethink and redeisgn how AI can fit into their business infrastructures like core workflows and operating models. The idea is evolutionary: How can AI work in tandem with human counterparts as an unstoppable team? This “speak‑think‑do” capability is where agents create real value. Instead of just conversing about a task, they act on it: decomposing goals, choosing tools, and executing steps.
This can apply to so many areas within a company. For example, in sales and marketing – it can look like a continuous pipeline of monitoring, account research, dynamic lead qualification, personalized outreach, and meeting prep. Tasks like this and more could all be running in the background so reps can focus on high‑leverage moments. Forrester’s 2025 emerging‑tech list explicitly calls out agentic AI for near‑term process automation potential. (Forrester).
Areas like Finance as well, collection assistants that prioritize outreach by risk, period‑close prep, and automated variance analysis are moving from pilots into enterprise suites; Oracle (Oct 2025) announced new agents across Fusion Applications for finance and beyond. (Oracle).
Even in IT & Employee Productivity too. A 24/7 helpdesk agent can help or totally dominate resolving common tickets, orchestrating access requests, and provisioning software - reflecting how leading ITSM platforms are packaging agent capabilities in 2025. (ServiceNow)
This evolution is the next step in AI – the biggest challenge currently is making sure it’s targeting the right areas. This will take time, research, and testing. Gartner cautions (via a June 2025 report) that over 40% of agentic AI projects may be canceled by 2027 due to cost and unclear value; Reuters reported the same prediction, highlighting “agent‑washing” and early‑stage maturity. The takeaway: focus on scoped problems with measurable outcomes, not just demos. (Gartner)
There are so many ways companies can thoughtfully employ AI in a way that can make a long term, scalable impact. Examples like targeting needle-moving workflows: choosing multiple step processes with clear KPIs (e.g., “first‑contact resolution +10 pts,” “time‑to‑quote −30%”). (McKinsey & Company). Defining the “unit of work” and boundaries - you must be explicit about the agent’s goal, inputs/outputs, and where human approvals apply; connect the agent to the systems it must act in. (This aligns with how Google positions Gemini Enterprise as a workplace “front door” to agents and apps.) Picking the right execution style is important as well - Using browser‑driven control for apps without APIs (e.g., Operator; Gemini Computer Use), API‑driven for structured systems, and multi‑agent patterns for parallel subtasks (as demonstrated by Anthropic’s multi‑agent research system and AWS examples).
Perhaps the most important of all? Noticing what works, and cutting away the rest.
Agentic AI is no longer theoretical. 2025 is the year it moved from demos to targeted production use. The platforms from Google, OpenAI, AWS, and Anthropic are enterprise‑ready, and the only thing holding it back is the actual process of changing our mindsets and workflows. Agents as coworkers may be shocking at first, but it allows humans to focus on the most important aspects of their jobs - increasing efficiency and success in all aspects.