Reimagining Enterprise AI

Steve Brooks recently sat down with Varun Goswami, Global Head of Product and AI at Newgen Software. They talked about AI, everything from reactive tools to autonomous, trustworthy agents.

How

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is Agentic AI different from traditional GenAI tools like chatbots or copilots?

Varun: Agentic AI represents the next leap in enterprise intelligence. While traditional GenAI tools, such as chatbots or copilots, are excellent at handling one-time tasks, Agentic AI moves beyond that. It operates autonomously, maintaining context and working toward defined objectives.

Think of it as evolving from a reactive assistant to a proactive digital colleague that can manage end-to-end processes.

For example, Financial Services. Instead of merely summarizing a transaction report, an Agentic AI system could monitor compliance, initiate corrective actions for anomalies, optimize liquidity management, and automatically generate regulatory filings. All this while ensuring adherence to complex frameworks like Basel III or IFRS.

This shift here is not about integrating smarter AI. It’s about building a goal-oriented ecosystem that collaborates with humans, helps make informed decisions, and delivers measurable outcomes responsibly.

What design principles enable this shift from stateless GenAI to goal-driven AI agents?

Varun: Three core principles underpin this evolution: context retention, modular orchestration, and embedded governance.

First, stateful architectures allow agents to retain context across interactions. This enables them to guide users through multi-step journeys such as mortgage applications, onboarding flows, or claims settlements without losing continuity or intent.

Second, modular design breaks complex processes into smaller, auditable models derived through an AI Agent. Each is capable of independent decisions yet orchestrated as a unified system. This makes AI both scalable and easier to monitor, update, and validate.

Finally, embedded governance ensures every agent operates within secure, permissioned boundaries. Policies, controls, and audit trails become part of the architecture, not afterthoughts.

Together, these principles transform AI from a convenience layer into a system of intelligence and accountability—capable of driving reliable, outcome-focused automation at enterprise scale.

How will multi-agent ecosystems build trust and ensure interoperability?

Varun: Trust has to be the cornerstone of any AI ecosystem, especially when multiple agents operate in regulated domains. Interoperability depends on open standards, auditability, and secure collaboration frameworks. An orchestration layer enables audit log verification coupled with access rights management, which can together ensure that every agent action is traceable and compliant.

Equally critical is reputation. Just like human professionals that earn trust over time based on transparency and consistency, AI Agents must demonstrate a proven track record of safe, effective, and compliant behaviour.

Beyond compliance, multi-agent ecosystems foster operational resilience. Agents can coordinate dynamically to resolve conflicts, escalate issues, or balance workloads, but only when the underlying infrastructure guarantees interoperability and trust. Agentic AI becomes that dependable workforce for companies when transparency is embedded into the system design from the ground up.

What kind of interfaces are required to govern and monitor autonomous agents effectively?

Varun: The observability layer has evolved from basic logging to real-time, multi-dimensional monitoring of agent behavior. To be impactful, it needs predictive analytics, explainability, and proactive anomaly detection for autonomous decision-making.

As enterprises grant AI more autonomy, human oversight must evolve. Future-ready interfaces will combine transparency with control, offering real-time visibility into an AI Agent’s rationale, decision pathways, and risk thresholds.

Interactive dashboards can help supervisors trace every step in a process, from document evaluation to rule application. Additionally, “what-if” simulators allow organizations to test agent behavior in complex or sensitive scenarios before deployment.

Agentic AI systems are designed with security and governance embedded from the outset. Enterprise-grade capabilities such as role-based access controls, encrypted data handling, built-in compliance checks, guardrails, and comprehensive audit trails ensure that data remains protected and every action is traceable.

Just as importantly, teams can continuously fine-tune policies, permissions, and guardrails as regulations, risks, and business priorities evolve. In this approach, governance is not an afterthought or an external control layer but a core design philosophy. It ensures automation remains transparent, explainable, and consistently aligned with human intent.

Why is agent orchestration becoming central to enterprise AI, replacing prompt engineering?

Varun: Prompt engineering was an early bridge to intelligent automation, but it struggles with multi-step, dynamic enterprise use cases. Agent orchestration changes the paradigm. It focuses on assembling networks of specialized agents, each operating autonomously yet in coordination. However, this introduces new challenges such as maintaining synchronized context and preventing compliance drift.

The solution lies in orchestration logic that governs data flow, enforces checks and balances, and embeds policy adherence into every transaction. It also requires mechanisms for conflict resolution, escalation, and exception handling so AI Agents don’t operate in isolation or misalignment.

In mature enterprise environments, the art of AI lies not in crafting prompts but in designing intelligent, compliant systems that think and act together. It enables them to scale safely, predictably, and in harmony with organizational goals.

What infrastructure and safeguards ensure the safe and responsible deployment of Agentic AI?

Varun: For enterprises, intelligence without accountability is a liability. A Zero Trust architecture forms the foundation for responsible Agentic AI. Access must always be permission-based, with encrypted data exchange, anonymization layers, and secure APIs.

Failsafe mechanisms, such as circuit breakers, real-time monitoring, and rollback triggers, ensure stability and compliance, even under anomalous conditions. Every decision should be traceable to a verifiable policy or rule. Agentic AI, therefore, is not merely a technological evolution but a governance revolution, combining autonomy with assurance and innovation with integrity.

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Newgen is the leading provider of AI-enabled unified digital transformation platform with native process automation, content services, and communication management capabilities. Globally, successful enterprises rely on Newgen’s industry-recognized low-code application platform to develop and deploy complex, content-driven, and customer-engaging business applications on the cloud. From onboarding to service requests, lending to underwriting, and for many more use cases across industries, Newgen unlocks simple with speed and agility.

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