By Sanjay Koppikar, Co-Founder and Chief Product Officer, EvoluteIQ
Enterprise operations have reached a decisive moment. For years, businesses relied on traditional automation for their operational efficiency. This method, however, is only effective for predictable, rule-based processes. The systems have inherent limitations due to its reactive and deterministic nature, often failing when there is complexity and/or change, for instance when data shifted, when exceptions multiplied, or when decisions required judgment rather than rote action. The imperative now is to adapt fully autonomous, collaborative agents.
In the Old-World Paradigm, AI was on the periphery of automation – enhancing the overall experience, speed or efficiency. AI was never the core. That was Industry 4.0.
I believe that since the arrival of Generative AI, we have entered into Industry 5.0 where AI has moved to centre of the business. AI-centric business or AI-First/AI-Led businesses transform the overall experience to the next level altogether.
And AI Agents are the primary drivers of this change.
The agentic transformation
So, what exactly are these “Intelligent Agents”?
Basically, a traditional bot follows instructions. You tell it what to do, step by step, and it executes. An AI Agent, on the other hand, understands goals. You tell it what you need to accomplish, and it figures out how to do it, adapting to obstacles, learning from outcomes, and making decisions along the way.
For example, in claims processing, a bot might extract data from a form and route it to the right department. An agent, however, can read the claim, cross-reference it against policy terms and historical patterns, identify inconsistencies, flag potential fraud, negotiate with other agents handling related claims, and decide whether to approve, reject, or escalate; all without human intervention.
Now imagine Multiple Agents working together. One agent handles document intake and classification. Another validates eligibility and coverage. A third manages pricing and contract terms. A fourth monitors for compliance violations. They don’t just operate in parallel; they communicate, negotiate, and collaborate. When one agent encounters an exception, it can consult another for context or delegate a subtask to a specialist agent better equipped to handle it.
This is what we call a multi-agent system. Each agent works independently yet stays in constant communication. This interplay of agents, each specialised, each accountable is what turns automation into intelligence.
But for this to work at enterprise scale, you need a structure. You need what we call an Agentic Mesh – a distributed, self-governing network where agents can find each other, communicate securely, negotiate responsibilities, and operate within defined business rules and compliance guardrails.
Think of it like a well-functioning organisation. Employees don’t operate in silos. They share information, resources, and/or collaborate with others. An Agentic Mesh creates that same dynamic intelligence across your systems. Agents discover each other’s capabilities, coordinate their actions, and adapt to changing conditions; all while staying within the boundaries you have set.
And within this mesh lies the foundation of the Convergent Agentic Intelligence Architecture. Think of it as the structure that holds everything together. It is not about bolting AI onto existing automation. It’s about building the entire platform around autonomous, collaborative agents as the foundational layer. Intelligence doesn’t come from one large model making all the decisions. It emerges organically from agents working together across different systems, each contributing their specialised capabilities to solve complex, dynamic problems that no single component could handle alone.
The automation layer still matters; it is the reliable operational backbone. But now, instead of rigid workflows that break when conditions change, you have intelligent agents that adapt, learn, and improve with every interaction.
A foundation for autonomous operations
To oversee a set of self-organising, self-enhancing agents, a control model that is centralised will not be sufficient. The defining prerequisite is an ‘Agentic Mesh Architecture’ that creates a distributed, self-governing mesh of intelligence. This mesh is based on several key layers for its operation:
- A Registry System serves as the thinking hub, holding a live directory of each agent’s name, abilities, performance record, and present availability. This fundamental capability allows agents to find and call upon the precise abilities they require dynamically, moving from static configuration-based deployment to dynamic, on-demand orchestration.
- Trust and Safety System guarantees security and governance as native aspects of the architecture. It maintains predefined guardrails, validates action against business policy, and offers immutable audit trails for all agent decisions and actions.
- Marketplace provides a platform for organisations to share, manage, and extend automation capabilities. It allows for the discovery, quality assessment and transparent licensing of agent services.
- A Communication System provides the strong protocols that are required for secure and advanced agent-to-agent (A2A) communication. Through a combination of dedicated message channels, optimised transfer protocols, and flexible collaboration patterns like delegation and consensus, it allows agents to coordinate, negotiate, and solve problems collectively in a seamless and scalable manner. Making it even more real, Agent Negotiations Protocol (ANP) is also getting in to make the A2A communication stronger through bringing in business rules, compliances, guidelines, guardrails, security among other negotiable and non-negotiable aspects of communication.
The power of convergence
Just the layer of ‘conversational agents’ does not yield good results. This was one of the reaons why a recent MIT study showed nearly 95% of Agentic AI based POVs failed for large enterprises. Any
Agentic AI layer that is built over classical automation building blocks with predictability shall give the confidence and real business values to the enterprises and those will sustain the adoption of new-age Agents as well.
The power of this agent-first architecture is the ability to consolidate different automation domains into one intelligent control plane. It employs adaptive, learning behaviour to what was previously hard and brittle:
- Process flows: Agents replace static workflows with dynamic decision-making. They autonomously handle exceptions, choose optimal paths based on contextual analysis, and continuously learn from execution outcomes to optimise processes.
- Data flows: Rigid Extract, Transform, Load (ETL) pipelines are replaced by adaptive, agent-powered data flows. These agents manage multi-modal data, automatically adjust to schema changes, and perform intelligent data cleansing and transformation based on semantic understanding.
- Event streams: Moving beyond simple rule-based pattern matching, agents apply sophisticated analytical capabilities to identify subtle, complex correlations within real-time event streams. This ability enables predictive forecasting and autonomous responses to events before they escalate.
- Robotic operations: Intelligent orchestrators manage robotic process automation (RPA) bots, providing the cognitive layer. This arrangement allows the bots to understand the content of an interface rather than only its screen coordinates. It also facilitates self-healing workflows, drastically reducing maintenance effort.
Conclusion: The autonomous enterprise
The future of business belongs to those organisations that successfully merge the reliability of deterministic automation with the adaptability of autonomous AI. This convergence represents a shift toward the Autonomous Enterprise. The evolution from limited, rule-bound bots to continuously learning, self-organising agents is now essential for maintaining competitive advantage in the modern economy. Businesses must rapidly adopt this new architectural foundation and begin the inevitable journey toward full operational autonomy.