Agentic AI at work

Generative AI has dominated recent headlines with its ability to produce everything from insights to strategies to code. But as organizations look beyond outputs toward outcomes, a more transformative development is taking shape: Agentic AI.

Agentic AI doesn’t just process information, it takes action. These autonomous systems are designed to pursue goals, make decisions, act and perceive in a real environment and adapt in real time with minimal oversight. Rather than waiting for prompts, they operate independently across complex workflows, driving execution as well as insight.

 

We have been helping organizations move from experimentation to execution with agentic AI. What follows are lessons from our experience, what makes agentic AI different, why it’s catching on and how we have seen it deliver tangible outcomes in real-world scenarios.

 

What sets agentic AI apart?

 

Agentic AI represents a shift from reactive tools to systems that operate with intent. Its value lies not in generating faster responses, but in its ability to act independently in service of defined outcomes. 

 

• Goal-driven behavior
  Unlike conversational chatbots that interact in real-time with humans based on their prompts, agentic AI operates with a defined goal and determines the best path forward. It can assess, plan and act independently.
  
  
• End-to-end execution
  Agentic AI doesn’t stop at a single query. It handles multi-step workflows, manages dependencies and adjusts in real time. This allows it to work effectively even when the process isn’t fully mapped out.
  
  
• Working through ambiguity
  It’s built on generative AI, agentic AI can operate in complex, uncertain environments. It can navigate unclear instructions, reconcile conflicting data and prioritize tasks, offloading decision-making that would otherwise require manual oversight.

 

Why is agentic AI gaining momentum?

 

Agentic AI is gaining traction because it delivers measurable outcomes where it matters most:

 

• Faster insights: It crunches massive volumes of data in seconds, transforming decision-making from retrospective to predictive.
  
  
• Operational efficiency: By automating workflows and repetitive tasks, it reduces both time and cost.
  
  
• Workforce productivity: Employees can shift focus from low-value tasks to work that drives innovation and customer impact.
  
  
• New value creation: Agents can bring context and combine both your internal and external data, explore interesting relationships and use tools to act, uncovering opportunities for value that pre-existing processes might not have discovered before.

 

These aren’t hypothetical benefits. Let’s look at where agentic AI is already making a difference.
 

Real-World use cases

 

1. Accelerating drug discovery at Bayer

 

Bayer partnered with us to integrate agentic AI into its R&D pipeline. The result: AI research assistants capable of sorting through thousands of preclinical study reports to identify relevant insights in seconds.

Previously, data scientists spent days writing and executing SQL queries to get the same answers. Now, decisions are made faster, redundant work is minimized and the cost of experimentation is reduced, ultimately benefiting patients. “Our agentic AI system acts as assistive memory, retrieving relevant information to make data-driven decisions faster and more accurately.”  Jonas Münch, Head of IT for Safety and Pharmacology, Bayer.

 

2. Automating report generation with multi-agent systems

 

One of the most powerful implementations we have delivered involves multi-agent collaboration. A single agent can execute a task, but a system of agents can orchestrate complex work.

 

Here’s an example of how we have set that up:

 

• Agent 1: Gather data from thousands of historical reports

• Agent 2: Synthesizes the data into a report tailored for a specific audience or regulatory format

• Agent 3: Reviews the content, verifies accuracy and flags inconsistencies

 

The system dramatically reduces reporting time, improves quality and frees scientists to focus on higher-impact work.

 

3. Improving knowledge work across the enterprise

 

In many organizations, highly-skilled employees spend a disproportionate amount of time on repetitive yet critical tasks, such as locating internal data, formatting documents or compiling status reports.

 

Agentic AI can automate these behind-the-scenes workflows. For example:
 

• Data discovery and aggregation are streamlined as agents retrieve, classify and organize information across siloed systems.

• Permission-aware access ensures that sensitive data is only surfaced to authorized users, an approach we implemented in the PEXA case to reduce risk while improving responsiveness.

   

Result: Increased confidence in AI tools, fewer manual errors and more time for teams to focus on client engagement, strategic planning and innovation.

 

4. Enhancing call center efficiency

 

Agentic AI is also reshaping customer service. From handling routine tasks like password resets to offering personalized solutions, AI agents now support call centers in meaningful ways. This doesn’t just streamline operations, it frees support teams to prioritize complex customer needs, improving both efficiency and satisfaction. 

 

Risks and how to manage them

 

Like any transformative technology, agentic AI comes with risks:

 

• Integration costs: Building and deploying custom agents isn’t cheap. Focus early investments on clear use cases with high ROI.
  
  
• Data privacy: Strong data governance is essential. Permission-aware access and encryption must be foundational.
  
  
• Opacity in decision-making: A lack of transparency can erode trust. With agents performing multi-step decisions, it is even harder to evaluate them.
  
  
• Evolving standards: Protocols like MCP have been gaining traction to standardize how agents can retrieve context and use tools, however protocols for agents to collaborate across heterogeneous environments, such as A2A release by Google in April 2025,  are still evolving and might require rework.
  
  
• Unbalanced human-in-the-loop involvement: It is difficult to determine the right level of human oversight and intervention, often resulting in either humans becoming bottlenecks again or too little involvement causing costly and ethical consequences.

 

Start small. Validate early. Scale with confidence. Reassess often. 

 

How to get started?

 

For leaders looking to bring agentic AI into their organizations:

 

• Lead with business value
  Focus on where agentic AI can deliver real outcomes, streamlining operations, accelerating decisions or improving customer experiences. Ground the case in business impact, not buzzwords.
  
  
• Build AI literacy across teams
  Equip stakeholders with a working understanding of what agentic AI can and can’t do. This creates alignment around opportunities and limitations and sets the foundation for effective human–AI collaboration.
  
  
• Map processes before automating
  Start by understanding your current workflows and value streams. Identify pain points, inefficiencies and areas where human effort is stretched. You may not need agents right away but this clarity helps surface where they could make a meaningful difference.
  
  
• Experiment in low-risk environments
  Look for internal processes with limited downstream impact, spaces where it’s safe to test, fail and learn. This gives teams room to explore how agents can support human collaboration without introducing undue risk.
  
  
• Blend automation with expertise
  Human oversight is critical, especially for nuanced decisions.
  
  
• Invest in iteration
  Use pilots and feedback loops to refine systems before wider rollout.
  
  
• Be prepared for change
  As the technology evolves and protocol standards emerge, design flexible architectures that can change and adapt quickly.

 

Realizing the value of agentic AI
 

We have seen firsthand how agentic AI can fill the gaps between insight and action. Whether it’s automating research, streamlining reporting or enabling more confident decisions, the value is tangible. This isn’t about replacing people, it’s about giving teams the tools to move faster, think smarter and focus on what matters most.

 

Curious where to begin with agentic AI? We’re here to share what’s worked for us and help you uncover high-impact opportunities for your organization. Let’s explore what agentic AI can do, together.