The convergence of scientific computing and engineering is accelerating innovation in unprecedented ways.

As different sectors seek to tackle complex physical systems, optimize design and simulation, and unlock the next wave of scientific breakthroughs, Capgemini’s association with Wolfram stands as a powerful milestone. Together, we’re combining decades of expertise in symbolic computation, generative AI, and systems engineering to create what we call the Capgemini co-scientist framework—an intelligent assistant built for engineering rigor.

At the heart of this collaboration lies a shared belief: generative AI is transformative, but it must be grounded in scientific accuracy, auditability, and domain reasoning to truly serve the engineering and scientific community. To enable the robustness that scientists expect from their tools, Wolfram Language brings all the infrastructure needed for a scientific reasoning engine: unmatched breadth of symbolic computation, algorithmic modelling, and knowledge representation, resulting in a co-scientist that doesn’t just generate answers in a typical LLM way—it actually understands the problem and queries verifiable facts to produce trustworthy results.

From Natural Language to Verified Computation

What makes the co-scientist novel is its ability to go beyond text-based generation. By combining large language models with Wolfram’s symbolic reasoning capabilities and curated computational knowledgebase, users can input a natural language query and receive responses that are not only contextual but computationally synthesized.

Imagine asking co-scientist to simulate the thermal behaviour of a new material under varying conditions—or to optimize the control logic of a complex mechatronic system. Rather than simply returning a list of suggestions, the co-scientist can use real Wolfram Language code to compute precise equations, model dynamic systems, and integrate directly with engineering workflows in different environments.

Hybrid AI in Action

This collaboration brings the vision of Hybrid AI to life—an approach that blends language model fluency with symbolic reasoning, scientific simulation, and rigorous rules. It’s this hybridization that unlocks reliability and traceability in safety-critical domains such as aerospace, automotive, and industrial automation.

Hybrid AI enables iterative co-design, traceable decision-making, and seamless collaboration between AI systems and human experts. Our joint solution with Wolfram represents a concrete step toward AI systems that are not only assistive but trustworthy.

Engineering a Better Future, Together

Capgemini’s Augmented Engineering strategy is about more than just productivity—it’s about elevating human expertise through AI, enabling organizations to solve harder problems faster. Our work with Wolfram builds a bridge between natural language interfaces and the rigorous world of scientific computing, ultimately empowering engineers, researchers, and product teams to think more freely, design more confidently, and innovate more responsibly.

As this collaboration evolves, we are excited to bring the power of the co-scientist to real-world use cases—from sustainability analytics and advanced manufacturing to systems engineering and intelligent product development. This is the future of engineering: collaborative, explainable, and scientifically grounded.

Read more about the collaboration with Wolfram here

Intelligent decision-making has never been so important, and agentic AI is a technology that can deliver the actionable insights the chief supply chain officer needs to build resilience and agility.

Intelligent decision-making has never been so important, and agentic AI is a technology that can deliver the actionable insights the chief supply chain officer needs to build resilience and agility.

To call the current business climate volatile is an understatement – and at enterprises across multiple industrial sectors, the people most keenly impacted by the resulting uncertainty are likely those responsible for managing their organization’s supply chains. These vital, logistical links are subject to powerful external forces – from economic and political factors to environmental impacts and changes in consumer behavior. It’s critical that the executives in charge of supply chains, and their teams, take advantage of every tool to make smarter decisions.

New, multi-AI agent systems can deliver the insights that not only make supply chains more resilient, but also help executives identify opportunities to reduce logistics costs. But organizations must be ready to take advantage of these powerful tools. Preparing for success includes creating the right roadmap and engaging the right strategic technology partner.

Common pain points in the chain

In my conversations with chief supply chain officers, I’ve identified several common pain points they’re keen to address. Most are being challenged to improve supply planning, reduce inventory cycle times and costs, better manage logistics investments, and do a better job of assessing risks associated with suppliers and other partners across their ecosystem.

A company’s own data is an important source of the information required to help CSCOs achieve these goals and to enable agentic AI. Unfortunately, legacy business intelligence systems are not up to the task. There are several ways in which they fail to deliver:

• Analytics systems rarely support strategic foresight and transformative innovation – instead providing business users with yet another dashboard.
• The results are often, at best, a topic for discussion at the next team meeting – not sufficient for a decision-maker to act upon immediately and with confidence.
• Systems typically fail to personalize their output to provide insights contextualized for the person viewing them – instead offering a generic result that satisfies nobody.
• Systems often aggregate data within silos, which means their output still requires additional interpretation to be valuable.

In short, many legacy systems miss the big picture, miss actionable meaning, miss the persona – and miss the point.

Based on my experience, I recommend an organization address this through multi-AI agent systems.

With the introduction of Gen AI Strategic Intelligence System by Capgemini, this could be the very system that bridges the gap between the old way, and a value-driven future. This system converts the vast amounts of data generated by each client, across their enterprise, into actionable insights. It is agentic: it operates continuously and is capable of independent decision-making, planning, and execution without human supervision. This agentic AI solution examines its own work to identify ways to improve it rather than simply responding to prompts. It’s also able to collaborate with multiple AI agents with specialized roles, to engage in more complex problem-solving and deliver better results.

How would organizations potentially go about doing this?

Establish an AI-driven KPI improvement strategy

First, organizations must establish a well-defined roadmap to take full advantage of AI-enabled decision-making – one that aligns technology with business objectives.

For CSCOs, this starts by identifying the end goals – the core business objectives and associated KPIs relevant to supply chain management. These are the basis upon which the supply chain contributes to the organization’s value, and strengthening them is always a smart exercise. The good news is that even small improvements to any of these KPIs can deliver enormous benefits.

The roadmap should take advantage of pre-existing AI models to generate predictive insights. It should also ensure scalability, reliability, and manageability of all AI agents – not just within the realm of supply chain management, but throughout the organization. That also means it should be designed to leverage domain-centric data products from disparate enterprise resource planning and IT systems without having to move them to one central location.

Finally, the roadmap must identify initiatives to ensure the quality and reliability of the organization’s data by pursuing best-in-class data strategies. These include:

• Deploying the right platform to build secure, reliable, and scalable solutions
• Implementing an enterprise-wide governance framework
• Establishing the guardrails that protect data privacy, define how generative AI can be used, and shield brand reputation.

An experienced technology partner

Second, the organization must engage the right strategic partner – one that can provide business transformation expertise, industry-specific knowledge, and innovative generative AI solutions.

Capgemini leverages its technology expertise, its partnerships with all major Gen AI platform providers, and its experience across multiple industrial sectors to design, deliver, and support generative AI strategies and solutions that are secure, reliable, and tailored to the unique needs of its clients.

Capgemini’s solution draws upon the client’s data ecosystem to perform root-cause analysis of KPI changes and then generates prescriptive recommendations and next-best actions – tailored to each persona within the supply chain team. The result is goal-oriented insights aligned with business objectives, ready to empower the organization through actionable roadmaps for sustainable growth and competitive advantage.

Applying agentic AI to the supply chain^*

Here’s a use case that demonstrates the potential of an agentic AI solution for supply chain management.

An executive responsible for supply chain management is looking for an executive-level summary and 360-degree visualization dashboard. They want automated insights and recommended next-best actions to identify savings opportunities.

An analytics solution powered by agentic AI can incorporate multiple KPIs into its analysis – including logistics spend, cost per mile, cycle time, on-time delivery rates, cargo damage, and claims. It can also track performance of third-party logistics service providers – including on-time performance, adherence to contractual volumes, freight rates, damages, and tender acceptance.

The solution can then apply AI and machine learning to optimize asset use through better design of loadings and routes. Partner performance can be analyzed – including insights into freight rates, delays, financial compliance, and lead times – and used to negotiate better rates.

The impact of this can include a reduction in logistics spend of approximately 10 percent, an opportunity to save approximately five percent through consolidation of routes and services, and a 15 percent improvement in transit lead time.

Capgemini enables this use case through an AI logistics insights 360 solution offered for the Gen AI Strategic Intelligence System by Capgemini. Just imagine this agent working 24/7 on your behalf; they don’t sleep, they don’t get tired, they don’t take vacation, and they’re completely autonomous.

Real results that relieve supply chain pressures

Capgemini’s modeling suggests that with the right implementation and support, the potential benefits include reducing overall supply chain spending by approximately five percent – including a 10-percent reduction in logistics spend. Other benefits include a three percent improvement in compliance, plus 360-degree order visibility and tracking.

Given that today’s supply chains are being subjected to so many pressures from so many sources, those are meaningful advantages that cannot be ignored.

^*Results based on industry benchmarks and observed outcomes from similar initiatives with clients. Individual results will vary.

The Gen AI Strategic Intelligence System by Capgemini works across all industrial sectors, and integrates seamlessly with various corporate domains. Download our PoV here to learn more or contact our below expert if you would like to discuss this further.

We’ve entered a new phase of AI – one where systems no longer wait for instructions but actively reason, plan, and act. This shift from generative to agentic AI raises a defining question:

Who will lead the next wave of transformation?

 Will it be the tech companies building the foundational models and platforms, or the industries embedding AI into real-world business workflows? The answer is clear: neither side can win alone. Agentic AI isn’t a plug-and-play solution—it’s a systemic leap that demands AI-native infrastructure, new talent roles, a culture of experimentation, and trust in autonomous systems. The future belongs to those who can bridge the gap between breakthrough technology and scalable, responsible value creation. In this article, we explore the evolving power dynamic between builders and adopters—and why service providers may be the unlikely accelerators of this new era.

Agentic AI: Beyond Implementation to Transformation

Unlike prior tech cycles, Agentic AI isn’t simply implementing a new tool or channel. It demands a complete rethink of how work is done, how decisions are made, and how value is created. To truly harness its power, industries need more than APIs and dashboards.

They need:

• Infrastructure readiness: scalable compute, data pipelines, and model orchestration.
• Talent transformation: from prompt engineers to AI product managers, the skills needed are nascent and niche.
• Mindset shift: a culture of experimentation, agility, and comfort with co-creating alongside AI.

In this context, the true differentiator isn’t just having access to AgenticAI; it’s being prepared to reimagine how you operate with AI at the core.

ROI, Talent, and the Black Box Problem

While tech companies dazzle with breakthrough models and autonomous agents, industries face grounded realities:

• ROI is uncertain unless use cases are tightly coupled with business outcomes.
• Niche talent is hard to find, and even harder to retain.
• The black-box nature of LLMs challenges observability, governance, and trust.
• Security, privacy, and compliance must be rethought in the age of generative automation.

This isn’t a plug-and-play revolution. It’s a systemic shift. Industries must invest not only in tools but also in readiness and resilience.

The Evolving Power Dynamic

Tech companies lead the way in building foundational models, toolchains, and agentic platforms. They control the tech stack, drive innovation velocity, and shape the ecosystem. Yet, they face challenges around monetization, trust, and the long tail of enterprise needs.

On the other hand, industries hold the real-world context, proprietary data, and deep knowledge of customer behaviour. They define high-value use cases, drive adoption at scale, and ultimately determine where AI delivers impact. But they must also tackle integration complexity, change management, and readiness gaps.

The new power players will be those who can navigate both worlds — translating the potential of Agentic AI into practical, governed, and scalable transformation across domains.

Strategic Implications for Service Providers

For service companies working with both tech builders and enterprise consumers, this creates a unique strategic opportunity:

• Act as translation layers between Agentic AI innovation and industry needs.
• Provide platformization strategies (moving from isolated tools and pilots to creating scalable, reusable AI foundations inside an enterprise) to help industries build internal capability, not just consume tech.
• Build AI governance frameworks that bridge the black-box risks and enterprise trust requirements.
• Offer talent incubation and skilling programs tailored to AI-first roles.

Service companies must evolve from implementation partners to AI transformation enablers.

The Real Winners: Co-Creators of Value

Ultimately, the winners in the Agentic AI era will not be defined solely by who builds the most powerful models or the most dazzling demos. They will be the ones who can:

• Align AI with business strategy.
• Drive adoption with speed and responsibility.
• Build ecosystems that are trustworthy, explainable, and human-centric.

This is not just a race to innovate — it’s a race to transform. And those who can blend technology, context, and trust will define the next era of value creation.

In this new landscape, co-creation is the new competitive advantage.

Capgemini and NVIDIA’s strategic collaboration provides an innovative AI solution designed to transform the way enterprises build and scale AI factories.

This work is aimed to assist organizations, particularly those in regulated industries or with substantial on-premises infrastructure investments, deploy agentic AI into their operations. By leveraging NVIDIA AI Enterprise software, accelerated infrastructure, and the Capgemini RAISE platform, companies can expect a seamless, high-performance AI solution ready for the future.

Managing AI at scale

Capgemini RAISE is our AI resource management platform, able to manage AI applications and AI agents across multiple environments within a single managed solution. This enables organizations to separate their solution from systemic risk and, leveraging NVIDIA NIM microservices, can centralize AI evaluation, AI FinOps, and model management. The business can then focus on delivering AI-augmented work, while the AI Risk Management team focuses on managing risk, costs, and technical challenges. 

This is a paradigm shift, placing the AI Factory at the center – and not only for private implementation, but as the global point for AI management.

“This new collaboration with NVIDIA marks a pivotal step forward in our commitment to bringing cutting-edge AI-powered technology solutions to our clients for accelerated value creation. By leveraging the power of the NVIDIA AI Stack, Capgemini will help clients expedite their agentic AI journey from strategy to full deployment, enabling them to solve complex business challenges and innovate at scale.” – Anne-Laure Thibaud, EVP, Head of AI & Analytics Global Practice, Capgemini

Benefits for modern enterprises

Imagine the ability to deploy agentic AI capabilities with a single click. Our partnership extends the reach of the Capgemini RAISE platform, bringing these capabilities to NVIDIA’s high-performance infrastructure. This enables companies to realize value more swiftly, and reduce total cost of ownership and deployment risk. Additionally, with the NVIDIA Enterprise AI Factory validated design, we guide organizations in building on-premises AI factories leveraging NVIDIA Blackwell and a broad ecosystem of AI partners.

Some of the other key features to support the navigation of complex, agentic AI solutions include:

• Rapid prototyping and deployment: Speeding up the deployment of AI agents through ready-to-use workflows and streamlined infrastructure, minimizing time-to-market.
• Seamless integration: Embedding AI agent functionalities into current business systems to enhance automation, operational efficiency, and data-informed decision-making.
• Scalability and governance: Deploying AI agents within strong governance models to ensure regulatory compliance, scalability, and consistent performance. Capgemini RAISE provides specialized agentic features – such as governance, live monitoring, and orchestration – to provide centralized management and measurable outcomes.

Scaling AI in private, on-premises environments

Our solution is designed to help organizations rapidly scale AI in private, on-premises environments. It supports key requirements such as data sovereignty and compliance to meet regulatory and data residency mandates. It also ensures resiliency and high availability for business continuity, security, and privacy controls for air-gapped environments. This solution delivers ultra-low latency for a diverse set of real-time use cases like manufacturing or healthcare imaging, and edge or offline use cases for remote, disconnected environments.

Capgemini RAISE and Agentic Gallery: Demonstrating the future

Alongside NVIDIA, we are bringing the power of Capgemini RAISE to on-premises infrastructure. This open, interoperable, scalable, and secure solution paves the way for widespread AI adoption. To illustrate our capabilities, we are launching the Agentic Gallery, a showcase of innovative AI agents designed to address diverse business needs and drive digital transformation.

Capgemini and NVIDIA have collaborated on over 200 agents, leveraging the NVIDIA AI Factory to create a robust ecosystem of AI solutions. This collaboration has led to the development of the Agentic Gallery, which is set to revolutionize the way businesses approach AI.

Is your organization ready to place the power of an AI Factory at the center of its business? Get in touch with our experts below.

Capgemini’s innovative AI agent tool is helping organizations in the public sector and beyond to reduce the cost and time of modernizing their legacy applications

For years, and across industries, rapid developments in digitalization have been creating challenges for companies around adapting to technological change. Particularly challenging are “legacy issues” such as outdated applications or obsolete software that need transferring to current technologies to stay maintainable.

This has unfortunately resulted in us talking about legacy modernization for so long that the first modernizations are already due to be modernized again.

A legacy modernization often involves rewriting the existing application code almost completely, as the original solution was likely based on a different technology or programming language. A software development team could still do this work manually, but it would involve considerable effort.

This is where Gen AI-augmented software engineering comes into play. It allows the development team to automate repetitive tasks by outsourcing them to generative AI. But while providing developers with simple, recurring code fragments is an exciting way to increase productivity and reduce costs, it only marginally reduces the effort involved in a legacy modernization. As a result, these projects remain manual, time-consuming and costly.

Figure 1: Capgemini research: Turbocharging software with AI
https://www.capgemini.com/insights/research-library/gen-ai-in-software/

How Capgemini’s AI agents are transforming legacy modernization

At Capgemini, we have developed an innovative approach that takes advantage of agentic AI coding agents to significantly reduce the time needed to modernize legacy applications.

Our AI agent tool – a sophisticated multi-agent system – is purpose-built to make legacy systems future-safe. We have designed it to support software teams in migrating custom-built applications from outdated technology stacks to modern platforms.

At the heart of the solution is the orchestration of a collaborative team of AI agents. This allows development teams to automate a large portion of the modernization process (see figure 2), resulting in a far more efficient, scalable approach to modernizing and migrating software.

Figure 2: Development focuses on defining what needs to be done and leaves much of the processing to the AI agents

Let’s call an AI agent to do the job

Unlike traditional chatbots that simply return responses, AI agents take ownership of tasks and actively drive them forward. They operate autonomously, optimizing based on new information or past mistakes. But they can also interact with large language models, other agents, or non-AI tools such as compilers.

In Capgemini’s AI agent tool, multiple agents collaborate to modernize a legacy application and transition it to a new technology stack. A human orchestrator defines the overall migration process, providing a structured set of instructions to guide the agents.

The instructions transfer Capgemini’s deep expertise to the agent, both in understanding the legacy system and in designing the target software architecture. They also determine the specific role each AI agent is assigned in the migration.

So that the transition runs smoothly, the roles of these AI agents mirror those in a human development team migrating a legacy application (see figure 3). A software developer agent analyzes the existing source code and rewrites it using the target technology. A testing or quality assurance (QA) agent then validates the code against predefined test cases. If any tests fail, the QA agent provides detailed error messages and returns the code to the developer for revision.

Once the code has passed all the tests, a DevOps agent takes over to build the complete application and checks it for runtime issues. In this way, every function of the original application is faithfully reimplemented in the new technology stack.

Figure 3: Get the job done – the power of agentic AI agents

An applicable approach across sectors

At Capgemini, we are already using this approach with many clients in the global public sector and beyond.

A German organization, for example, was looking for a solution to modernize its approximately 40 outdated applications. The client could not develop those applications any further but also recognized the need to integrate new features and switch to a modern technology platform.

Migrating all those legacy applications manually would have been very time-consuming and costly. Thanks to our AI agent tool, though, a large part of this previously manual migration could be automated. The amount of development effort needed dropped correspondingly, and the project costs fell significantly – freeing up the client to concentrate on developing innovative features.

Would you like to try Capgemini’s AI agent tool for yourself?

By automating the process, our tool makes it faster and more cost-effective to switch legacy applications over to new and future-safe technologies.

What’s more, as every migration path is different, we customize our tool to the modernization context each time.  The enablement team will support you with analyzing your specific migration paths and conducting pilots.

Finally, in case you are wondering, the question of sovereignty does not play a role here. That is because the AI agents run both in your public cloud environment and “air-gapped” on-premise.

Agentic AI marks a paradigm shift from reactive AI systems to autonomous, goal-driven digital entities capable of cognitive reasoning, strategic planning, dynamic execution, learning, and continuous adaptation with a complex real-world environment. This article presents a technical exploration of Agentic AI, clarifying definitions, dissecting its layered architecture, analyzing emerging design patterns, and outlining security risks and governance challenges. The objective is strategically equipping the enterprise leaders to adopt and scale agent-based systems in production environments.

1. Disambiguating Terminology: AI, GenAI, AI Agents, and Agentic AI

Capgemini’s and Gartner’s top technology trends for 2025 highlight Agentic AI as a leading trend. So, let’s explore and understand various terms clearly.

1.1 Artificial Intelligence (AI)

AI encompasses computational techniques like symbolic logic, supervised and unsupervised learning, and reinforcement learning. These methods excel in defined domains with fixed inputs and goals. While powerful for pattern recognition and decision-making, traditional AI lacks autonomy, memory, and reasoning, limiting its ability to operate adaptively or drive independent action.

1.2 Generative AI (GenAI)

Generative AI refers to deep learning models—primarily large language and diffusion models—trained to model input data’s statistical distribution, such as text, images, or code, and generate coherent, human-like outputs. These foundation models (e.g., GPT-4, Claude, Gemini) are pretrained on vast datasets using self-supervised learning and excel at producing syntactically and semantically rich content across domains.

However, they remain fundamentally reactive—responding only to user prompts without sustained intent—and stateless, with no memory of prior interactions. Crucially, they are goal-agnostic, lacking intrinsic objectives or long-term planning capability. As such, while generative, they are not autonomous and require orchestration to participate in complex workflows or agentic systems.

1.3 AI Agents

An agent is an intelligent software system designed to perceive its environment, reason about it, make decisions, and take actions to achieve specific objectives autonomously.

AI agents combine decision-making logic with the ability to act within an environment. Importantly, AI agents may or may not use LLMs. Many traditional agents operate with symbolic reasoning, optimization logic, or reinforcement learning strategies without natural language understanding. Their intelligence is task-specific and logic-driven, rather than language-native.

Additionally, LLM-powered assistants (e.g., ChatGPT, Claude, Gemini) fall under the broader category of AI agents when they are deployed in interactive contexts, such as customer support, helpdesk automation, or productivity augmentation, where they receive inputs, reason, and respond. However, in their base form, these systems are reactive, mostly stateless, and lack planning or memory, which makes them AI agents, but not agentic. They become Agentic AI only when orchestrated with memory, tool use, goal decomposition, and autonomy mechanisms.

1.4 Agentic AI

Agentic AI is a distinct class where LLMs serve as cognitive engines within multi-modal agents that possess:

• Autonomy: Operate with minimal human guidance
• Tool-use: Call APIs, search engines, databases, and run scripts
• Persistent memory: Learn and refine across interactions
• Planning and self-reflection: Decompose goals, revise strategies
• Role fluidity: Operate solo or collaborate in multi-agent systems

Agentic AI always involves LLMs at its core, because:

• The agent needs to understand goals expressed in natural language.
• It must reason across ambiguous, unstructured contexts.
• Planning, decomposing, and reflecting on tasks requires language-native cognition.

Let’s understand with a few examples: In customer support, an AI agent routes tickets by intent, while Agentic AI autonomously resolves issues using knowledge, memory, and confidence thresholds. In DevOps, agents raise alerts; agentic AI investigates, remediates, tests, and deploys fixes with minimal human input.

Agentic AI = AI-First Platform Layer where language models, memory systems, tool integration, and orchestration converge to form the runtime foundation of intelligent, autonomous system behavior.

AI agents are NOT Agentic AI. An AI agent is task-specific, while Agentic AI is goal-oriented. Think of an AI agent as a fresher—talented and energetic, but waiting for instructions. You give them a ticket or task, and they’ll work within defined parameters. Agentic AI, by contrast, is your top-tier consultant or leader. You describe the business objective, and they’ll map the territory, delegate, iterate, execute, and keep you updated as they navigate toward the goal.

2. Reference Architecture: Agentic AI Stack

2.1 Cognitive Layer (Planning  and Reasoning)

• Foundation Models (LLMs): Core reasoning engine (OpenAI GPT-4, Anthropic Claude 3, Meta Llama 3).
• Augmented Planning Modules: Chain-of-Thought (CoT), Tree of Thought (ToT), ReAct, Graph-of-Thought (GoT).
• Meta-cognition: Self-critique, reflection loops (Reflexion, AutoGPT Self-eval).

2.2 Memory Layer (Statefulness)

To retain and recall information. This is either information from previous runs or the previous steps it took in the current run (i.e., the reasoning behind their actions, tools they called, the information they retrieved, etc.). Memory can either be either session-based short-term or persistent long-term memory.

• Episodic Memory: Conversation/thread-local memory for context continuation.
• Semantic Memory: Long-term storage of facts, embeddings, and vector search
• Procedural Memory: Task-level state transitions, agent logs, failure/success traces.

2.3 Tool Invocation Layer

 Agents can take action to accomplish tasks and invoke tools as part of the actions. These can be built-in tools and functions such as browsing the web, conducting complex mathematical calculations, and generating or running executable code responding to a user’s query. Agents can access more advanced tools via external API calls and a dedicated Tools interface. These are complemented by augmented LLMs, which offer the tool invocation from code generated by the model via function calling, a specialized form of tool use.

2.4 Orchestration Layer

• Agent Frameworks: LangGraph (DAG-based orchestration), Microsoft AutoGen (multi-agent interaction), CrewAI (role-based delegation).
• Planner/Executor Architecture: Isolates planning logic (goal decomposition) from executor agents (tool binding + result validation).
• Multi-agent Collaboration: Messaging protocols, turn-taking, role negotiation (based on BDI model variants).

2.5 Control, Policy & Governance

• Guardrails: Prompt validators (Guardrails AI), semantic filters, intent firewalls.
• Human-in-the-Loop (HITL): Review checkpoints, escalation triggers.
• Observability: Telemetry for prompt drift, tool call frequency, memory divergence.
• ABOM (Agentic Bill of Materials): Registry of agent goals, dependencies, memory sources, tool access scopes.

3. Agentic Patterns in Practice

(Source-OWASP)

As Agentic AI matures, a set of modular, reusable patterns is emerging—serving as architectural primitives that shape scalable system design, foster consistent engineering practices, and provide a shared vocabulary for governance and threat modeling. These patterns embody distinct roles, coordination models, and cognitive strategies within agent-based ecosystems.

• Reflective Agent : Agents that iteratively evaluate and critique their own outputs to enhance performance. Example: AI code generators that review and debug their own outputs, like Codex with self evaluation.
• Task-Oriented Agent :Agents designed to handle specific tasks with clear objectives. Example: Automated customer service agents for appointment scheduling or returns processing.
• Self-Learning and Adaptive Agents: Agents adapt through continuous learning from interactions and feedback. Example: Copilots, which adapt to user interactions over time, learning from feedback and adjusting responses to better align with user preferences and evolving needs.
• RAG-Based Agent: This pattern involves the use of Retrieval Augmented Generation (RAG), where AI agents utilize external knowledge sources dynamically to enhance their decision-making and responses. Example: Agents performing real-time web browsing for research assistance.
• Planning Agent: Agents autonomously devise and execute multi-step plans to achieve complex objectives. Example: Task management systems organizing and prioritizing tasks based on user goals.
• Context- Aware  Agent:  Agents dynamically adjust their behavior and decision-making based on the context in which they operate. Example: Smart home systems adjusting settings based on user preferences and environmental conditions. 
• Coordinating Agent :Agents facilitate collaboration and coordination and tracking, ensuring efficient execution. Example: a coordinating agent assigns subtasks to specialized agents, such as in AI powered DevOps workflows where one agent plans deployments, another monitors performance, and a third handles rollbacks based on system feedback.
• Hierarchical Agents :Agents are organized in a hierarchy, managing multi-step workflows or distributed control systems. Example: AI systems for project management where higher-level agents oversee task delegation.
• Distributed Agent Ecosystem: Agents interact within a decentralized ecosystem, often in applications like IoT or marketplaces. Example: Autonomous IoT agents managing smart home devices or a marketplace with buyer and seller agents.
• Human-in-the-Loop Collaboration: Agents operate semi-autonomously with human oversight. Example: AI-assisted medical diagnosis tools that provide recommendations but allow doctors to make final decisions.

4. Security and Risk Framework

Agentic AI introduces new and very real attack vectors like (non-exhaustive):

• Memory poisoning – Agents can be tricked into storing false information that later influences decision
• Tool misuse – Agents with tool or API access can be manipulated into causing harm
•  Privilege confusion – Known as the “Confused Deputy,” agents with broader privileges can be exploited to perform unauthorized actions
• Cascading hallucinations – One incorrect AI output triggers a chain of poor decisions, especially in multi-agent systems
• Over-trusting agents – Particularly in co-pilot setups, users may blindly follow AI suggestions

 5. Strategic Considerations for the enterprise leaders

5.1 Platformization

• Treat Agentic AI as a platform capability, not an app feature.
• Abstract orchestration, memory, and tool interfaces for reusability.

5.2 Trust Engineering

• Invest in AI observability pipelines.
• Maintain lineage of agent decisions, tool calls, and memory changes

5.3 Capability Scoping

• Clearly delineate which business functions are:
• LLM-augmented (copilot)
• Agent-driven (semi-autonomous)
• Fully autonomous (hands-off)

5.4 Pre-empting and managing threat

• Embed threat modelling into your software development lifecycle—from the start, not after deployment
• Move beyond traditional frameworks—explore AI-specific models like the MAESTRO framework designed for Agentic AI
• Apply Zero Trust principles to AI agents—never assume safety by default
• Implement Human-in-the-Loop (HITL) controls—critical decisions should require human validation
• Restrict and monitor agent access—limit what AI agents can see and do, and audit everything

5.5 Governance

• Collaborate with Risk, Legal, and Compliance to define acceptable autonomy boundaries.
• Track each agent’s capabilities, dependencies, and failure modes like software components.
• Identify business processes that may benefit from “agentification” and identify the digital personas associated with the business processes.
• Identify the risks associated with each persona and develop policies to mitigate those. 

6. Conclusion: Building the Autonomous Enterprise

Agentic AI is not just another layer of intelligence—it is a new class of digital actor that challenges the very foundations of how software participates in enterprise ecosystems. It redefines software from passive responder to active orchestrator. From copilots to co-creators, from assistants to autonomous strategists, Agentic AI marks the shift from execution to cognition, and from automation to orchestration.

For enterprise leaders, the takeaway is clear: Agentification is not a feature—it’s a redefinition of enterprise intelligence. Just as cloud-native transformed infrastructure and DevOps reshaped software delivery, Agentic AI will reshape enterprise architecture itself.

And here’s the architectural truth: Agentic AI cannot scale without platformization.

To operationalize Agentic AI across business domains, enterprises must build AI-native platforms—modular, composable, and designed for autonomous execution.

The future won’t be led by those who merely implement AI. It will be defined by those who platformize it—secure it—scale it.