Practical AI
Practicality of AI Increasing as Agentic Age Advances
Artificial intelligence in business used to look like a clever parlor trick. It could write emails, summarize documents, and maybe even draft some marketing copy, but it could not really do the work. Over the past year or two, that has changed dramatically as agentic AI has emerged, turning simple, responsive chatbots into goal-driven workers that can take meaningful and valuable action in real systems, follow multi-step plans, and operate inside real governance.
From Chatting to Acting
The defining shift is from chatting to acting. Instead of living off to the side as a “smart assistant,” modern AI agents plug directly into line-of-business applications through APIs and connectors. They can create and update CRM records, log support tickets, move money between accounts under policy, trigger DevOps pipelines, and execute other actions that used to require a human at the keyboard. Major enterprise platforms now ship with native integration to CRM, ERP, ITSM, and productivity suites, which means agents can work inside the systems that matter, not just comment on them from the outside.
That Four-Letter Word: PLAN
Just as important, agents have learned to plan. Early generative AI responses were single-shot. You asked a question, it wrote an answer, and that was that.
Today’s agentic systems can decompose a goal into a sequence of steps, decide which tools to use at each point, and then execute that plan across multiple systems. When you point an agent at an incident, a supply-chain disruption, or a compliance review, it is no longer restricted to a one-and-done answer. It can gather evidence, take actions, evaluate the results, and iterate until it either completes the task or reaches a defined boundary. It then comes back to you, the user, to continue what truly is a robust, productive conversation between manager and assistant.
That is much closer to how people actually work.
Going Multi-Agent
Another key development is the remarkable advance from solo agents to well-orchestrated, carefully managed AI “swarms” or “teams.”
Instead of one giant generalist trying to do everything, enterprises are increasingly deploying multiple specialized agents with clearly defined roles: a planner that breaks work down, executors that call specific tools and systems, reviewers that check outputs, and usually an agentic supervisor that coordinates all of them.
These multi-agent patterns—hierarchies, peer collaborations, even dynamic “swarms”—allow businesses to trade off speed, cost, and control for each process. In practice, this makes agents more reliable, because no single model is responsible for everything from strategy to execution and quality assurance.
All of this becomes far more practical when agents can maintain enough context in their “memory” to be useful. AI arranges information into “tokens” that it can then measure and manage. Long-context models now support a “context window” that can contain hundreds of thousands of tokens, in some cases approaching a million, so an agent can work over entire case files, long chat histories, or multi-system logs without being limited to a few snippets at a time.
This eliminates many brittle "chunking" tricks and reduces the errors that arise from not seeing the whole story.
At the same time, modern agent frameworks provide persistent state; they can remember where a workflow left off, pause to ask a human for input, and then resume later, which mirrors how real business processes unfold over days or weeks rather than seconds. This supervision keeps a human in the loop, which has emerged as a clear necessity to prevent inadvertent damage to data entities.
The Importance, as Always, of Governance
Everything AI begins with clean, well-structured data enabling reliable processing. None of this would be deployable at scale without better data and system governance. If you are going to allow software to act in your production environment, you need strong guardrails. Contemporary agent platforms recognize this and now include policy enforcement, sensitive data detection, prompt defenses, and explicit task boundaries as built-in features rather than afterthoughts.
Human-in-the-loop controls are now first-class as well, from approval checkpoints to escalation paths. Agents can propose actions, but nothing moves without the right level of human sign-off, especially in regulated industries. That is the difference between a lab demo and a production system your risk team can live with.
Equally important, agents are now built by non-developer IT and business teams. What were originally called “coding assistants” and “agent builder platforms” are now considered AI agents unto themselves designed for the purpose of taking plain English descriptions of required functionality from a user, planning out the entire system, dividing it into discreet tasks, and assigning those tasks to separate agents in parallel to accelerate production of the final product application. Even professional developers with years of coding experience acknowledge that they now relegate more than three-quarters of their coding to popular platforms like Claude Code or OpenAI Codex.
Standard interfaces and protocols are evolving so that tools, models, and platforms can easily interoperate, easing concerns about lock-in and integration pain. This lowers the barrier to entry and lets organizations treat agents as simply another layer of enterprise software, not as an exotic science project.
Measurable, Productive Results
The most important net result is that the benefits are now measurable rather than hypothetical. In sales and marketing, organizations are reporting double-digit improvements in lead conversion when they use AI agents to qualify, route, and continuously optimize outreach across channels. In customer service, agentic automation is cutting response times and allowing support organizations to absorb growth without hiring at the same expensive rate. IT and operations teams are seeing large reductions in manual workload as agents handle routine incidents, changes, and requests, while people focus on truly novel or high-risk situations. Finance, risk, and analytics teams are using agents as persistent monitoring and analysis layers, catching issues earlier and giving leaders faster, better-grounded recommendations.
Back to Where We Started
We launched “Practical AI” to encourage users to go beyond using AI chatbots as fun and fascinating toys and to explore the many practical ways AI could improve their businesses. In fact, perhaps the most important change in 2025 and 2026 is psychological. Businesses have moved past the “toy stage” of generative AI.
Regular use of these tools across business functions has become the norm, meaning people, processes, and platforms are now ready to adopt agents that do more than write nice emails. The vendor ecosystem has exploded, putting dozens of agent builders, orchestrators, and vertical solutions into the market, backed by significant investment and an emerging set of best practices.
And because organizations are now tracking concrete KPIs such as conversion rates, cycle times, error rates, and hours saved, it is much easier to justify moving from pilots to production.
In other words, agentic AI is making AI in business truly practical because it finally lines up with how business actually works: multi-step, multi-system, governed, and measurable. The technology has caught up with the reality of the working enterprise, and not a moment too soon.
About the Author
Technologist, creator of compelling content, and senior "resultant" Howard M. Cohen has been in the information technology industry for more than four decades. He has held senior executive positions in many of the top channel partner organizations and he currently writes for and about IT and the IT channel.