Beyond chatbots: a clear look at Agentic AI

By 2028, Gartner forecasts that 33% of enterprise software will incorporate agentic AI, 20% of digital storefront interactions will be conducted by AI agents, and 15% of day-to-day decisions will be made autonomously. Gartner emphasizes that agentic AI will introduce a goal-driven digital workforce: an extension of the human workforce that plans and acts independently, without breaks or benefits.

Agentic AI is shifting the conversation from what AI can say to what it can do. We’ve moved past simple prompt-response systems. The real power of today’s AI lies in its ability to reason, act, and iterate toward outcomes. This is what we call agentic AI: a new way to solve business challenges using intelligent, goal-oriented systems.

These agents are already in action. We’ve recently used them to structure data from multi-turn audio calls and empower frontline teams with real-time insights, among several other impactful applications. In every case, they helped reduce manual effort, complex decision making and unlock new value. Know what agents do. And what they don’t. That’s the key to building them right.

What is an AI agent?

An AI agent is a system designed to perceive, decide, and act autonomously towards a specific goal it was given.

It begins by ingesting virtually any kind of input, whether that’s a user question, a spreadsheet, a PDF, or even an audio clip, and enriching it through retrieval over your internal and external knowledge bases to ground its understanding in the most relevant, up-to-date context. During this phase, the agent not only identifies key entities and relationships but also surfaces background information, precedents and domain-specific data that help it interpret nuance and intent accurately.

Once that context is in hand, the agent moves into its decision-making process, where it reasons about possible approaches, reflects on past outcomes or leverages explicit planning algorithms. Large language models like GPT, Claude or Gemini often power these steps, providing the flexible, generative intelligence needed to weigh trade-offs, anticipate complications and craft an optimal path forward.

Finally, armed with a clear plan, the agent translates its decisions into concrete actions. This could mean calling external APIs to fetch or update data, running complex calculations or simulations, writing records to a database, kicking off other tools or workflows in your ecosystem, or any combination thereof. By continuously looping through these stages, reason, act, observe, the system adapts in real time, handles unexpected outcomes and steadily drives itself toward your specified goal.

Unlike static bots that follow a linear path, agents operate in a reasoning loop: they reason, act, observe the outcome, and decide what to do next. The most basic form of this  loop, known as ReAct (for Reason + Act), is what allows them to tackle complex, unpredictable tasks. Of course, you’ll find countless definitions of “agent” out there, and in practice it’s hard to draw a hard line: rather than a binary label, we prefer to think of systems on an agentic spectrum. Any workflow that closes the loop between input, decision and action falls somewhere along that spectrum, with some solutions exhibiting far more autonomy, and thus more “agentic” behavior, than others.

Where agentic AI shines

Let’s be real. AI agents aren’t needed for everything. But they excel in situations where there's a clear goal, uncertain or evolving inputs, and the need for decision-making along the way.

Here’s where they bring real value:

• Strategic Decision-Making: Agentic AI shines in evaluating trade-offs and selecting optimal paths, using reasoning and planning to choose actions that best align with complex multi-step workflows.
  
  
• Cross-Tool Integration: Agents seamlessly coordinate disparate APIs, databases and services, stitching together complex workflows without manual handoffs.
  
  
• Working with unstructured data: Emails, audio files, PDFs? No problem.
  
  
• 24/7 Autonomous Operation: Agentic AI can monitor, decide and act round-the-clock.
  
  
• Improving over time: With built-in reflection and feedback loops, they can learn and adapt.

We’ve deployed light agents that summarize documents, mid-level agents that assist sales teams, and even exploratory prototypes with full cross-system orchestration. The value doesn’t come from the buzzword, it comes from real outcomes: faster decisions, streamlined workflows, and more consistent results.

Under the hood

While agents may feel like magic, there’s serious engineering behind them.

They rely on:

• Language models for reasoning (GPT, Claude, Gemini, etc.)
  
  
• Prompt engineering and management to guide behavior. Effective prompting techniques are key to guiding agent behavior and ensuring reliable performance.
  
  
• Retrieval-Augmented Generation (RAG) to access real-time or domain-specific knowledge
  
  
• Tool integrations to act on their environment
  
  
• Orchestrators (like LangGraph or Semantic Kernel) to manage complex workflows
  
  
• Observability tools to monitor behavior and continuously improve
  

Agents can also be fine-tuned for specific use cases or industries, enhancing their relevance and accuracy. In some advanced cases, reinforcement learning techniques are applied to help agents adapt through trial and feedback.

The key is how everything fits together. Agentic AI isn’t just about picking a model. It’s about building and orchestrating a workflowthat can work towards a goal autonomously, from prompt to decision to action and back again.

Why now?

Agents themselves aren’t new, AI-driven assistants and rule‐based bots have been around for years. What’s changed is the democratization of AI and the emergence of large, generalist generative models that make genuinely autonomous, goal-oriented agents possible at scale. Generative AI gave us the spark. Agentic AI is the engine.

This evolution is reshaping software development itself, introducing new patterns for building intelligent, adaptive systems.

At Osedea, we’re already helping clients across industries turn this potential into real, working solutions. We see this as more than a trend; it’s a new design pattern for how AI will be built and used in business.

Curious if your use case could benefit from an AI agent? Let’s talk about how we can bring your vision to life.