The rise of goal-driven AI agents that think, plan, and act without immediate human supervision.

In recent years, the term “agentic AI” has risen to the forefront of enterprise technology discussions. Unlike traditional artificial-intelligence or machine-learning models that respond to prompts or perform narrow tasks, agentic AI refers to systems that can plan, reason, execute multi-step workflows, and adapt in-flight with limited human oversight.
These autonomous agents are increasingly being seen as the next evolutionary step in artificial intelligence: not just tools, but collaborators, co-workers, or digital labor.

The Business Imperative: Why Agents Matter
For enterprises, the shift toward agentic AI holds the promise of greater productivity, agility, and scale. According to , “30% of enterprises will automate more than half of their network activities by 2026” — and many of those initiatives will increasingly rely on autonomous agents.
In addition, a McKinsey report highlights that agentic AI can shift generative AI tools from reactive to proactive, enabling new revenue streams and operational agility.
Start-ups are responding accordingly: one investor report estimated that agentic-AI start-ups pulled in US $2.8 billion in funding in the first half of 2025 alone.

Technological Foundations: What Makes Agents Work
Behind agentic AI lie several converging technologies: large-language models (LLMs) and generative AI, reinforcement learning, multi-agent coordination, planning and memory systems, and real-time integration with enterprise workflows.
Additionally, business analysts note that successful agents require more than models — they need governed platforms and trust frameworks. For example, recently launched a “data trust” platform powered by an autonomous agent to build AI-ready data foundations.

Use-cases Across the Enterprise
Agentic AI is already appearing in multiple areas of enterprise operations:

• Customer service and support: Agents that interact with customers, escalate when needed, and learn from interactions.

• Sales & marketing: Agents that orchestrate campaigns, monitor outcomes, adjust in near-real time.

• IT operations: Autonomous ticketing, remediation, and incident triage using agentic platforms.

• Knowledge-work augmentation: Agents that support decision-making, research, document creation, and workflow automation.

• Supply-chain and logistics: Agents that manage order flows, handle exceptions, optimize routes, or stocking decisions.
  For each of these, the key is the agent’s ability to act on behalf of human stakeholders, recognize changes, adapt, and escalate as necessary — rather than simply executing static rules.

Challenges and Risks: Why Many Projects Fail
However, the agentic AI journey is far from trivial. According to Gartner, over 40% of agent-AI projects are expected to be cancelled by end of 2027. That is due to integration complexity, legacy workflows, lack of clear ROI, and immature governance.
Specific challenges include:

• Legacy systems and data silos: Agents work best when workflows, data, and models are connected.

• Trust, transparency and governance: Autonomous agents raise questions of accountability, bias, explainability.

• Change-management and culture: Agents can shift roles; human teams must adapt to working alongside autonomous systems.

• Measuring value: It’s harder to quantify the impact of agents (vs. simple bots) because they act across tasks and roles.

• Security and privacy: Autonomous systems with decision-making ability raise surface exposures and regulatory concerns. For example, recent legal action by against a startup for unauthorized agentic behaviour shows how real these risks are.

Best Practices for Deployment
To navigate the agentic-AI terrain, leading organisations recommend the following best practices:

1. Start with clearly defined, high-impact workflows where the agent can deliver measurable value. Avoid implementing agents just for novelty.

2. Establish the governance layer early — data trust, model reliability, agent-audit trails, escalation protocols.

3. Ensure human-in-the-loop and human-on-the-loop frameworks to monitor, intervene, and refine agent behaviour.

4. Align agentic-AI initiatives with the enterprise operating model: how workflows are defined, how teams work, how success is measured.

5. Monitor changes in roles and skills: As agents learn and act autonomously, human roles will evolve toward oversight, exception-handling, orchestration, and strategy.

Strategic Outlook: What’s Next?
Over the next 2-5 years, we can expect to see:

• More vertical-specific agentic solutions are emerging (e.g., finance, healthcare, manufacturing) as domain knowledge is embedded.

• Agents bridging across systems — not just front-office bots, but end-to-end workflow agents spanning digital and physical systems.

• Secondary markets forming around agent orchestration platforms: low-code or no-code agent marketplaces, marketplaces of “agent skills.”

• Emergence of hybrid human-agent teams where autonomous systems handle routine, structured decision-flows while humans focus on strategy, judgement and exceptions.

• Increased regulatory focus and ethical frameworks around autonomous agents: who is responsible when an agent acts, how to ensure fairness, transparency and safety.

Closing Thoughts and Looking Forward

Agentic AI represents a transformational leap from automated tools to autonomous collaborators. For organisations with the right domain, data, and operational maturity, agents offer the potential to scale human judgment, accelerate decision-making, and unlock new sources of value. But the path is not simple. Projects require careful design, strong governance, and cultural alignment.

As we move into 2026 and beyond, companies that treat agents not just as “bots that follow rules” but as strategic orchestration engines will lead. The question is no longer whether to invest in agentic AI — but how to deploy it wisely, govern it responsibly, and ensure it complements rather than disrupts human potential.

For enterprise digital specialists and C-level leaders alike, now is the time to ask: what workflows could our autonomous agents handle? How will we measure their impact? And how will our organisation evolve to harness this new class of digital co-workers?

Author: Serge Boudreaux – AI Hardware Technologies, Montreal, Quebec
Co-Editor: Peter Jonathan Wilcheck – Miami, Florida

References:

1. “Establishing Digital Trust In The Agentic AI Era” (Forbes) – https://www.forbes.com/councils/forbestechcouncil/2025/11/07/establishing-digital-trust-in-the-agentic-ai-era/

2. “Autonomous generative AI agents: Under development” (Deloitte Insights) – https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/autonomous-generative-ai-agents-still-under-development.html

3. “Hyperautomation and the Rise of AI-Driven Business Processes” (TechBullion) – https://techbullion.com/hyperautomation-and-the-rise-of-ai-driven-business-processes/

4. “The 10 Hottest Agentic AI Tools And Agents Of 2025 (So Far)” (CRN) – https://www.crn.com/news/ai/2025/10/hottest-agentic-ai-tools-and-agents-of-2025-so-far/

5. “Agentic AI: How Autonomous Agents Are Revolutionizing Business” (IEEE/TechNews) – https://www.computer.org/publications/tech-news/trends/agentic-ai-business