Counting agents is the wrong scoreboard: Better questions for enterprise leaders

Date:
Fri, 30 Oct 2026 14:56:21 +0000

Description:
The organizations getting real value from AI are not the ones with the
longest agent inventory, they are the ones who never framed the problem that way to begin with.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter Ask a room of executives about agentic AI and within about ninety seconds someone will ask how many agents they ought to be running. It has become the default measure of progress: a number you can put on a slide, benchmark against a competitor, and report upwards.

It is also the wrong question. Kevin Kimber Social Links Navigation

Senior Vice President for Oracle UK & Ireland. Across the 200-plus workshops my team and I have run with enterprises in Europe and the Middle East, the organizations getting real value from AI tools are not the ones with the longest agent inventory. They are the ones who never framed the problem that way to begin with. They started by identifying a business problem or point of friction and worked backwards. Everyone else started from the technology and went looking for somewhere to put it. Latest Videos From TechRadar Watch full video here:

There are four questions I hear constantly that are quietly steering good companies into Pilot Purgatory. Here they are, and here is what I would ask instead. Don't ask how many agents. Ask what outcome you are improving. Much of the last two years of enterprise AI has been about helping people work faster. Copilots that answer questions, assistants that summarize the meeting you have just sat through, tools that get you past the blank page. These are genuinely useful and I would encourage everyone to switch them on. You may like Stop choosing the wrong Agentic AI use cases The next AI phase is better agents not bigger models 'The test isnt whether AI can do something. Its whether it can make the process measurably better': We hear why businesses need to be more selective about where theyre using AI

But speed is not the same as value, and agent count is not the same as
either. No enterprise ever created value by accumulating agents. Value shows up in real business outcomes such as fewer payroll errors, faster shift scheduling, lower supplier sourcing costs, better win rates, cash collected sooner.

That is the difference between an agent and an agentic application. An agent help with an individual task. An agentic application coordinates teams of specialized agents to deliver a business outcome. If you cannot name the KPI an agent is moving, you do not have a use case. You have a demo. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting
your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over. Don't ask whether AI can recommend. Ask whether it can finish the job. The most persistent confusion I encounter is between advice and execution. Plenty of systems can now sense a situation, spot the pattern and tell you what should happen next. That is real progress. It also leaves almost all of the work exactly where it was.

Take sourcing. An assistant can flag that a category is overdue for renegotiation and suggest you run an event. Someone still has to create that event, chase the responses, score them against commercial criteria, route the approvals correctly, update the contract and shepherd the whole thing to a close.

An agentic application does that work, under policy, with human oversight and approval where required, and with a record of what it did and why. What to read next The task trap: Why AI's biggest promise has nothing to do with
speed AI agents are ready to act, but do they understand your business? How
AI agents will change how people work and what they need from a PC

The test is simple. At the end of the interaction, is the process further forward, or do you just have a better-informed human facing the same queue? Don't ask which model tops the leaderboard. Ask whether it understands your business. Benchmark scores dominate the AI conversation and matter far less
in the enterprise than most leaders assume. Raw model intelligence is rarely the binding constraint.

A frontier model understands procurement as a discipline. It does not understand your supplier list, your delegation-of-authority matrix, your contractual obligations, your current inventory position, or the three exceptions your finance director signed off in March. Without that, sophistication amounts to a well-argued guess.

Enterprise context is the differentiator: trusted enterprise data and
business context, including the policies, approvals, transaction history, organizational structure and live process state that already sit inside your core systems. The closer agentic capabilities are to the systems where business processes, data, security, and approvals already live, the more effectively agents can understand context and act within establish controls.

Don't ask for one system that does everything. Ask how the specialists coordinate.

There is a persistent hope that enterprise AI will eventually resolve into a single, omniscient digital assistant. Businesses do not work that way, so their AI will not either.

Organizations are ecosystems of specialists: finance, HR , supply chain, service, sales, each with its own expertise, obligations and objectives. The equivalent in AI is not one agent that knows everything, but coordinated
teams of agents that know their own domain and work towards a shared goal.

Hiring is a good illustration. One agent surfaces and screens candidates. Another prepares onboarding. A third confirms the budget is genuinely available. A fourth keeps the hiring manager's approvals moving.
Individually, each is modestly useful. Together they move a business process from vacancy to first day. The value sits in the coordination, not the components. Systems of outcomes What connects all four questions is a shift
in what enterprise software is actually for. For decades our applications recorded what had happened. We are now entering the era of systems of outcomes: software that reads the state of the business, reasons over it, and acts to keep work moving towards a defined objective.

That shift demands different questions from leadership. Not how many agents, but which outcome. Not can it advise, but can it execute. Not how clever is the model, but how well does it know us. Not one brain, but one team.

Get the questions right and the technology decisions become considerably easier. Get them wrong and you will assemble an impressive collection of agents, tools, and models that nobody can tie to a number that matters. We've featured the best AI website builder. This article was produced as part of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



======================================================================
Link to news story: https://www.techradar.com/pro/counting-agents-is-the-wrong-scoreboard-better-q uestions-for-enterprise-leaders


--- Mystic BBS v1.12 A49 (Linux/64)
* Origin: tqwNet Technology News (1337:1/100)