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Gaming Gear

The path to Agentic AI: overcoming complexity to embrace the autonomous enterprise

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The future of enterprise AI isn’t just about insights – it’s about a monumental evolution of how businesses buy and sell in the global economy.

AI agents are poised to take automation beyond any capabilities that we’ve witnessed to date, shifting from AI tools that assist decision-making to independently thinking entities that augment execution at scale.

Deloitte predicts that by 2027, half of all companies will use GenAI to launch agentic AI pilots or proofs of concept, marking a significant transformation in how businesses operate.


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Monish Darda

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CTO and Co-Founder, Icertis.

Challenges on the Path to Agentic Adoption

While agentic AI holds immense promise, organizations must first overcome multiple hurdles. Case in point: Another recent survey found that more than 85 percent of enterprises will require upgrades to their existing technology stack in order to deploy AI agents. Most businesses are still in the early stages of AI adoption, and scaling agentic workflows from initial investments to drive enterprise-wide ROI remains a major challenge.

The road to agentic AI requires rethinking IT infrastructure, ensuring seamless and quality data integration, addressing security and compliance risks, and fostering organizational trust in autonomous solutions – all while ensuring the right guardrails are in place. Without a well-defined strategy, companies risk inefficiencies, implementation barriers, reputational risk, and missed opportunities to harness AI’s full potential.

Complexity in Scaling

Agents individually aren’t enough. They can’t be deployed in isolation and need to work in coordination across systems to execute complex multi-step processes – manifesting as agentic workflows. Unlike monolithic systems with predictable interactions, an agentic workflow orchestrates a network of AI agents to solve intricate and layered problems autonomously with machine-scale analysis and human in the loop decision making.

Businesses need advanced orchestration frameworks capable of managing these complex interactions, ensuring robust error handling and maintaining workflow continuity across teams. Developing a clear roadmap will be critical in helping organizations deploy and scale AI agents effectively.

Accountability and Governance

With multiple agentic workflows operating independently yet collaboratively, ensuring accountability is a major challenge. Without a well-defined governance model, businesses risk a lack of oversight, which can lead to noncompliance, financial discrepancies, and reduced trust in AI-driven processes. Agents need to understand the rules of business that humans follow – rules that are defined by legal frameworks, ethical practices, and captured in contracts between customers, suppliers, and partners.

By “gut checking” decisions against contractual terms before taking action and ensuring clear audit trails are in place across the business, agentic decision-making becomes transparent and traceable, and far less likely to result in unnecessary liability.

Ensuring Data and Privacy

In any enterprise system, it’s critical for organizations to handle sensitive information responsibly and securely. Before deploying agentic workflows, ensure that data is clean and structured so sensitive information may be used by multiple agents simultaneously without exposure.

This applies to bank account details that are necessary for supplier payments, employee personal information, and contract data, as prime examples. Businesses should also establish secure data pipelines and continuous compliance measures to mitigate risks while enabling AI agents to function effectively and responsibly.

Trust and Change Management

Adopting agentic workflows requires more than just technical capability – it demands cultural change. Many organizations struggle with trusting AI agents due to concerns about reliability, accuracy, bias, ethical implications, and lack of transparency.

In fact, a recent study revealed data output quality and security and privacy concerns are among the top 10 barriers to AI adoption. Resistance to change within organizations, combined with a lack of understanding of how AI agents work, can create obstacles.

For businesses to fully embrace agentic AI, increase AI literacy and awareness around how AI agents operate with internal training and a top-down call to action driven by leadership. Emphasizing security protocols and privacy protections will also help to build confidence.

The First Step Toward an Autonomous Enterprise

So where can businesses realize immediate value from AI agents and agentic workflows?

AI agents are only as good as the data they train on. If enterprises want to drive profitability and capture returns from their AI strategy, they should start by looking at the data that drives the flow of commerce. Commercial agreements and the critical data they contain are foundational to how enterprises buy and sell, while also providing the compliance constraints agents need to do their jobs well without adding layers of risk.

The path to agentic AI is not a straight line. Yet by strategically addressing challenges, businesses can unlock new levels of intelligence and operational efficiency to embrace their future as an autonomous enterprise.

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This article was produced as part of TechRadarPro’s Expert Insights channel where we 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/news/submit-your-story-to-techradar-pro



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A New Law of Nature Attempts to Explain the Complexity of the Universe
Gaming Gear

A New Law of Nature Attempts to Explain the Complexity of the Universe

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Kauffman argues that biological evolution is thus constantly creating not just new types of organisms but new possibilities for organisms, ones that not only did not exist at an earlier stage of evolution but could not possibly have existed. From the soup of single-celled organisms that constituted life on Earth 3 billion years ago, no elephant could have suddenly emerged—this required a whole host of preceding, contingent but specific innovations.

However, there is no theoretical limit to the number of uses an object has. This means that the appearance of new functions in evolution can’t be predicted—and yet some new functions can dictate the very rules of how the system evolves subsequently. “The biosphere is creating its own possibilities,” Kauffman said. “Not only do we not know what will happen, we don’t even know what can happen.” Photosynthesis was such a profound development; so were eukaryotes, nervous systems and language. As the microbiologist Carl Woese and the physicist Nigel Goldenfeld put it in 2011, “We need an additional set of rules describing the evolution of the original rules. But this upper level of rules itself needs to evolve. Thus, we end up with an infinite hierarchy.”

The physicist Paul Davies of Arizona State University agrees that biological evolution “generates its own extended possibility space which cannot be reliably predicted or captured via any deterministic process from prior states. So life evolves partly into the unknown.”

“An increase in complexity provides the future potential to find new strategies unavailable to simpler organisms.”

Marcus Heisler, University of Sydney

Mathematically, a “phase space” is a way of describing all possible configurations of a physical system, whether it’s as comparatively simple as an idealized pendulum or as complicated as all the atoms comprising the Earth. Davies and his co-workers have recently suggested that evolution in an expanding accessible phase space might be formally equivalent to the “incompleteness theorems” devised by the mathematician Kurt Gödel. Gödel showed that any system of axioms in mathematics permits the formulation of statements that can’t be shown to be true or false. We can only decide such statements by adding new axioms.

Davies and colleagues say that, as with Gödel’s theorem, the key factor that makes biological evolution open-ended and prevents us from being able to express it in a self-contained and all-encompassing phase space is that it is self-referential: The appearance of new actors in the space feeds back on those already there to create new possibilities for action. This isn’t the case for physical systems, which, even if they have, say, millions of stars in a galaxy, are not self-referential.

“An increase in complexity provides the future potential to find new strategies unavailable to simpler organisms,” said Marcus Heisler, a plant developmental biologist at the University of Sydney and co-author of the incompleteness paper. This connection between biological evolution and the issue of noncomputability, Davies said, “goes right to the heart of what makes life so magical.”

Is biology special, then, among evolutionary processes in having an open-endedness generated by self-reference? Hazen thinks that in fact once complex cognition is added to the mix—once the components of the system can reason, choose, and run experiments “in their heads”—the potential for macro-micro feedback and open-ended growth is even greater. “Technological applications take us way beyond Darwinism,” he said. A watch gets made faster if the watchmaker is not blind.

Back to the Bench

If Hazen and colleagues are right that evolution involving any kind of selection inevitably increases functional information—in effect, complexity—does this mean that life itself, and perhaps consciousness and higher intelligence, is inevitable in the universe? That would run counter to what some biologists have thought. The eminent evolutionary biologist Ernst Mayr believed that the search for extraterrestrial intelligence was doomed because the appearance of humanlike intelligence is “utterly improbable.” After all, he said, if intelligence at a level that leads to cultures and civilizations were so adaptively useful in Darwinian evolution, how come it only arose once across the entire tree of life?

Mayr’s evolutionary point possibly vanishes in the jump to humanlike complexity and intelligence, whereupon the whole playing field is utterly transformed. Humans attained planetary dominance so rapidly (for better or worse) that the question of when it will happen again becomes moot.

Illustration: Irene Pérez for Quanta Magazine

But what about the chances of such a jump happening in the first place? If the new “law of increasing functional information” is right, it looks as though life, once it exists, is bound to get more complex by leaps and bounds. It doesn’t have to rely on some highly improbable chance event.

What’s more, such an increase in complexity seems to imply the appearance of new causal laws in nature that, while not incompatible with the fundamental laws of physics governing the smallest component parts, effectively take over from them in determining what happens next. Arguably we see this already in biology: Galileo’s (apocryphal) experiment of dropping two masses from the Leaning Tower of Pisa no longer has predictive power when the masses are not cannonballs but living birds.



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