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Artificial Intelligence: The "Agentic" Shift

Information Technologies | David Steele Friday, December 5, 2025

OVERVIEW

Woman with glasses looking through code

The article explores the evolution of AI from reactive generative tools to proactive, goal-oriented Agentic AI , which autonomously plans and executes complex tasks. This shift is transforming industries by automating workflows, enhancing efficiency, and enabling AI -native software engineering. However, it emphasizes the importance of trust, governance, and human- AI collaboration to harness this potential responsibly and effectively.

INDEPTH

The conversation around Artificial Intelligence is evolving. For the past few years, the focus has been on generative AI tools that respond to specific prompts, creating text, images, or code on command. While powerful, this represents just one facet of AI 's potential. A more advanced capability is emerging, known as Agentic AI . This marks a shift from passive, reactive systems to proactive, autonomous agents that can reason, plan, and execute complex, multi-step tasks to achieve a defined goal. This article explores the "agentic" shift, its impact on business, and the frameworks needed to manage it effectively.

The Capabilities of Agentic AI

So, what separates an AI agent from a standard generative AI model? The core difference lies in autonomy and goal-orientation. While a tool like ChatGPT waits for your next instruction, an agentic system is designed to pursue a larger objective independently. You provide the goal, and the AI agent breaks it down into smaller tasks, executes them, and adapts its approach based on the results.

Think of it this way: you could ask a generative AI to draft an email to a potential client. You need to provide the context, the key points to include, and the desired tone. An agentic AI , on the other hand, could be given the goal: "Secure a meeting with the head of marketing at Company X." The agent could then:

  1. Research: Analyze Company X's website and recent news to understand their business priorities.
  2. Identify: Find the correct contact person through professional networking sites.
  3. Draft & Send: Compose a personalized outreach email that references the company's recent achievements or challenges.
  4. Follow-up: If no response is received, it could send a polite follow-up email after a set period.
  5. Schedule: Once a positive response is received, it can access your calendar, propose available times, and book the meeting directly.

This ability to automate entire workflows is transforming industries. In finance, agentic systems can perform market analysis, execute trades based on predefined strategies, and generate performance reports without human intervention. A 2023 report noted that financial firms using AI for tasks such as algorithmic trading and portfolio management saw significant improvements in efficiency and decision-making speed. For supply chain management, an AI agent could monitor inventory levels, predict demand based on market trends, and automatically place orders with suppliers to prevent stockouts.

The Rise of AI Governance Platforms

As AI becomes more autonomous, the need for robust oversight becomes critical. Handing over complex business processes to an AI requires a deep level of trust. How do you know the agent is acting reliably, ethically, and in alignment with your organization's policies? This is where AI governance platforms come into play.

These platforms are essential for managing the risks associated with powerful AI . They provide a framework for transparency, compliance, and control. Trust is the currency of this new AI -driven landscape. Organizations must be confident that their AI systems are not producing biased outcomes or "hallucinating" incorrect information that could lead to poor business decisions. A 2024 survey found that over 60% of enterprise leaders cited "trust and reliability" as their primary concern when adopting advanced AI systems.

AI governance platforms help address these concerns by:

  • Monitoring Performance: Tracking the AI 's actions and decisions in real-time to check for anomalies or deviations from expected behavior.
  • Ensuring Compliance: Building in regulatory and ethical guidelines to help the AI operate within legal and corporate boundaries.
  • Providing Transparency: Creating a clear audit trail that explains why the AI made a particular decision, making the AI 's "black box" more understandable to human stakeholders.
  • Managing Bias: Testing and refining models to identify and mitigate biases that could lead to unfair or inaccurate outcomes, especially in areas like hiring or lending.

Implementing a strong governance structure is not just about mitigating risk; it's about enabling innovation. When you can trust your AI , you can confidently deploy it to solve more complex and valuable problems for your business.

AI -Native Software Engineering

The world of software development is also experiencing a profound transformation driven by agentic AI . For years, AI has assisted developers with tasks like code completion and bug detection. However, the agentic shift is pushing this collaboration to a whole new level, leading to what is now termed AI -native software engineering.

In this new paradigm, AI is not just a helpful assistant; it is an active participant in the entire software development lifecycle. Imagine an AI agent tasked with building a new application feature. It could write the initial code, create and run unit tests to check for errors, deploy the code to a staging environment, and even manage the release process. According to recent industry analysis, development teams using AI -driven tools have reported up to a 40% increase in productivity, allowing them to focus on higher-level system design and innovation.

This approach does not replace human developers. Instead, it augments their abilities, creating a collaborative partnership between human and machine. Developers transition from writing every line of code to supervising AI agents, defining architectural requirements, and validating the final product. Technology becomes the tool, and human expertise directs its application toward the solution. This allows teams to build, test, and deploy software faster and more reliably than ever before.

Charting Your Course in the Agentic Era

The shift toward agentic AI is more than a technological trend; it represents a fundamental change in how we can approach work, problem-solving, and innovation. From automating complex business workflows to building more resilient software, autonomous AI agents offer a path toward greater efficiency and capability. However, harnessing this power requires a thoughtful strategy, one that places a premium on trust, governance, and human- AI collaboration.

Understanding how these advancements can be applied to your specific business context is the first step. We work directly with our clients to create a partnership, serving as an extension of your team to explore these possibilities.

If you are ready to explore how agentic AI can reshape your operations and drive growth, let's connect. Schedule an AI Discovery Session with our team, and together we can dig into the possibilities for your organization.

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ABOUT THE AUTHOR

David Steele is the co-founder of Intrada Technologies, a full-service web development and network management company launched in 2000.  David is responsible for developing and managing client and vendor relationships with a focus on delivering quality service.  In addition, he provides project management oversight on all security, compliancy, strategy, development and network services.

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