Agentic AI: Everything you need to know

What is agentic AI?

Agentic AI refers to artificial intelligence that doesn't just respond to commands. It takes action on its own. Unlike traditional AI, which passively waits for human input, agentic AI observes, decides, and executes tasks independently.

Think of it like an AI personal assistant - not the rudimentary chatbots we've become accustomed to, but the next evolution of this. AI agents don't just remind you about a meeting but reschedule it proactively when they detect a conflict, book a ride to the venue based on traffic, and follow up with notes afterward.

At its core, Agentic AI has three defining traits:

Autonomy: It can operate with minimal human intervention, deciding what to do next.

Adaptability: It learns from its environment and adjusts its behaviour accordingly.

Goal-oriented action: It doesn't just process data; it actively works toward an objective. This shift from passive to proactive AI is what makes agentic AI so powerful - and why it's reshaping industries from finance to manufacturing.

Reasoning: It evaluates situations, draws inferences, and makes informed decisions by synthesizing available data.

Beyond boundaries: How agentic AI can redefine business collaboration

The real power of agentic AI lies in its ability to foster collaboration, both within a business and across multiple businesses.

Inside a business: AI agents could autonomously help manage HR processes, inventory optimisation, marketing campaigns, and supply chains, all while communicating seamlessly to achieve the company's broader goals.

Between businesses: Imagine the AI agent of a logistics company dynamically working with a retailer's procurement agent and a supplier's production agent. Together, they optimise delivery schedules, adapt to changing demand, and reduce inefficiencies across the supply chain.

This kind of inter-company collaboration represents a powerful ecosystem where agentic AI doesn't just improve internal operations - it transforms how businesses interact. If your company isn't thinking, preparing, and experimenting on these internal opportunities and wider ecosystem changes, you risk being outpaced by competitors who are.

How is agentic AI different from traditional AI?

Agentic AI stands out because it doesn't just respond to a request to analyse data - it proactively acts. Effectively, it's autonomy vs guidance. Agentic AI operates autonomously, making independent decisions based on its programming and environment, while traditional AI and GenAI typically require human input or structured data to function

Meet your new travel assistant

Picture this: You're planning a holiday to Paris. Today you might be amazed by an AI helping you search for flights or recommend a hotel when you ask. But an agentic AI? It'll do so much more:

• Know your travel plans and book flights proactively when prices drop.
• Rearrange your itinerary if your flight is delayed.
• Recommend restaurants based on your dining preferences and even book tables for you.
• Flag that your passport is about to expire - and help you renew it.

Agentic AI doesn't just react; it thinks ahead, adapts, and makes decisions autonomously.

Now, scale that to a business level. A company leveraging agentic AI might see its supply chain agents optimising inventory across regions, while marketing agents run dynamic campaigns tailored to real-time customer insights. This isn't just about improving efficiency. It's about transforming how work gets done.

The game-changing benefits of agentic AI

Agentic AI promises a wealth of benefits for businesses, both now and in the future as the technology continues to evolve and adapt.

1. Proactive problem-solving

Agentic AI anticipates issues before they arise. Example: A logistics company using agentic AI to reroute trucks in real time, avoiding traffic and optimising fuel use.

2. Human-AI collaboration

AI agents are beginning to take over repetitive, low-value tasks. Soon, they'll attend meetings for you, write detailed RFPs, evaluate vendor responses, and provide actionable recommendations. This allows people to focus on higher-value, strategic work.

3. The power of ecosystems

Imagine businesses seamlessly collaborating through their AI agents. For example, a retailer's procurement agent could work directly with a supplier's production agent to minimise delays, reduce waste, and save costs. The result? A powerful, interconnected ecosystem where businesses work together effortlessly.

The risks and challenges of agentic AI

Agentic AI is a revolutionary tool, but like all technology, it's not infallible. Let's return to your proactive travel assistant. It knows you're planning a holiday, so, with its usual enthusiasm, it jumps into action.

It books your flight, reserves a taxi to the airport, arranges fast-track security, handles automatic check-in and even speeds you through friendly immigration (your agents and the immigration agents having connected, worked together and approved your travel without you having to fill in a single multiple-page visa form).

Everything seems seamless... until you find yourself in Sydney, Nova Scotia, not Sydney, Australia.

This kind of mistake is a stark reminder of the risks of agentic AI, not just in terms of getting it wrong, but also for security and trust. Handing over personal or sensitive information to AI agents is a big deal and requires an element of trust. While incredibly capable, AI agents are still only as good as their programming, data, and training.

Why did this happen?

1. Misinterpretation of context: Your agent didn't clarify your intended destination and assumed Sydney, Nova Scotia, based on incomplete data. It might have based its decision on your recent searches for something different, or confusion of matching information.
2. Poor validation processes: The system didn't cross-check flight details against your other travel history.
3. Autonomy without oversight: The agent acted without confirming critical decisions with you.

How can agentic AI risks be mitigated?

Businesses and individuals using agentic AI must plan for potential errors by implementing safeguards.

Guardrails and clear validation rules: AI systems should require confirmation for critical decisions, like booking expensive flights or choosing destinations.

Explainability and transparency: Your AI should provide detailed reasoning for its actions, helping you understand why it made a particular choice.

Human-in-the-loop systems: For complex or high-stakes decisions, an AI agent should always include a human reviewer to ensure accuracy.

Common sense reasoning in AI: AI systems should incorporate "common sense agents" that question their own reasoning and assumptions before acting. For example, the AI might self-assess: "Sydney, Canada seems unlikely; I should ask for clarification before proceeding".

These protections don't just apply to travel, they're critical for businesses as well. Imagine an agentic AI managing supply chains accidentally overriding inventory or breaching compliance rules. Without oversight and validation processes, the costs could be catastrophic.

Why businesses must embrace agentic AI in 2025

Agentic AI isn't just another tech upgrade - it's a shift in thinking on how work gets done. Companies that successfully integrate agentic AI won't just be faster, they'll be smarter, more adaptive, and more collaborative. Those that hesitate? They risk being outpaced, outperformed, or adopting AI in ways that harm rather than help.

Many organisations are already embracing generative AI, which is sure to lead to a further adoption of agentic AI. McKinsey estimates that generative AI could automate up to 70% of business activities by 2030, adding $2.6 trillion to $4.4 trillion to the global economy. From here, it's only a short hop and a jump to agentic AI.

This will come from a range of opportunities; some we have not fully considered.

Radical operational efficiency: Agentic AI optimises workflows, eliminates inefficiencies, and accelerates decision-making. Instead of waiting on reports, businesses get real-time insights and automatic corrective actions. A next-gen logistics company using AI sees delivery times shrink and costs drop as AI reroutes shipments dynamically.

Hyper-personalised customer experiences: AI agents anticipate customer needs, tailor interactions, and automate responses dynamically. Businesses that personalise in real-time will deepen loyalty and increase revenue. Imagine the travel platform using AI adjusts bookings, suggests itinerary changes, and manages disruptions automatically.

AI-driven innovation & competitive advantage: Agentic AI doesn't just optimise, it creates new business models. AI-native companies will automate intelligently, collaborate seamlessly, and move at machine speed, leaving slower competitors behind.

AI as the new infrastructure: AI-driven companies will operate autonomously, optimise resources in real-time, and make data-driven decisions instantly. The future belongs to those who embrace AI now.

Case study: Accelerating data retrieval

Last year we started helping one of UK's foremost intelligence organisations with AI and data retrieval. The business was facing the growing challenge of handling vast volumes of data within a legacy platform, leading to operational bottlenecks, inaccurate or incomplete retrieval, and a less-than-ideal user experience.

By incorporating Generative AI, the team aimed to streamline how information was identified and consumed, ensuring only the most relevant insights were served to analysts. This required an end-to-end solution that not only improved search and ranking but also infused intelligent personalisation - ultimately delivering the right content to the right people, at the right time.

Agentic architecture means you have multiple AI “agents,” each responsible for a distinct function, working together autonomously toward a shared goal. Typically for a use-case like this there could be an ingestion agent proactively gathering large volumes of incoming data from various sources, while a transformation agent cleans and structures that data for analysis.

A ranking agent then orders search results based on relevance, and a personalisation agent tailors those results to individual analysts. When user feedback or ambiguous signals arise, a feedback agent could loop in a "human-in-the-loop" for review, ensuring oversight and mitigating errors. By coordinating these specialised agents seamlessly, the entire system stays adaptable, transparent, and efficient, even as data volumes and complexity grow.

Along the way, the project offered critical lessons, such as the importance of having a human-in-the-loop for expert oversight, cost mitigation strategies for controlling AI resource usage, and designing a flexible architecture that can integrate cutting-edge technologies over time. This application underscores how agentic AI can deliver tangible value by streamlining workflows, boosting efficiency, and ensuring transparency in decision-making.

The future of agentic AI: Transforming industries and lives

The future of agentic AI is one of quiet disruption. AI agents will work seamlessly in the background, optimising systems, adapting to our needs, and quietly making everything work smarter and faster. Whether it's managing global supply chains, personalising healthcare, or revolutionising education, agentic AI is set to transform industries and lives alike.

Embracing agentic AI with Colibri Digital

At Colibri Digital, we're helping businesses understand and embrace this transformation. Our Ignition Workshops start that understanding by equipping organisations to:

• Identify opportunities for agentic AI.
• Implement responsible, effective systems.
• Future-proof their operations for the agentic AI era.