AI agents are no longer just an experiment in R&D labs—they’re becoming digital employees in modern organizations. And just like any employee, they need tools, responsibilities, oversight, and a clear place in the org chart.
In a recent episode of Everyday AI, Cognizant’s CTO of AI, Babak Hodjat, offers one of the most grounded and insightful discussions we’ve heard yet on multi-agent systems. More than a buzzword, multi-agent AI represents a fundamental evolution in how enterprises can structure, scale, and streamline decision-making.
AI Agents as Digital Employees
We often frame AI agents at NewTide as “digital employees.” This lens helps clarify both the power and the risks. Just as you wouldn’t drop a new hire into the company without onboarding, access control, and a manager, you shouldn’t deploy agents without designing their scope, tools, and escalation paths.
Babak echoed this idea in describing how Cognizant “agentified” their Neuro platform—replacing modular software with autonomous agents that can communicate, delegate, and reason with one another. What used to take 10–12 weeks for a proof of concept now takes 10–15 minutes. That’s not just automation—it’s enterprise agility, reimagined.
Why Now? The Conditions Are Finally Right
Multi-agent AI isn’t new, but we finally have the ingredients for scalable adoption: powerful large language models for reasoning, rich APIs for execution, and natural language as the interface.
This creates a perfect storm for companies overwhelmed by complexity. As Babak puts it, many enterprises are already deploying agent-like systems—they just haven’t called them agents yet. Now the question is whether those agents are collaborating effectively… or operating in silos.
From Forms to Conversations
One example: Babak described a recent life event—his son turning 26—and how his company’s internal multi-agent intranet system handled it. Rather than navigating forms across HR, payroll, and benefits portals, he simply typed: “My son just turned 26.”
The system recognized implications, contacted the relevant “departmental” agents, and even offered to book celebratory time off. It’s the kind of contextual intelligence you’d expect from a high-performing assistant—and it’s only possible through coordinated digital employees acting across domains.
Getting It Right: Redundancy, Not Just Autonomy
As with any workforce, giving too much autonomy without oversight can create real risk. Babak warns about cascading misalignment—when agents reinforce one another’s errors. It’s a valid concern, and it’s why redundancy, governance, and human-in-the-loop design are critical.
Again, the analogy to digital employees holds. You want cross-checks, performance reviews, escalation channels. You want them trained on the right data, empowered with the right tools, and aligned with the company’s values and goals.
Where to Start? Think Organizational Design
The first step isn’t technical—it’s organizational. Start by identifying repetitive or slow-moving workflows. Internal systems (HR, finance, IT support) are often ideal candidates. Think about which “jobs” a digital employee could take over, and how you’d manage them if they were human.
From there, design for collaboration. Agents shouldn’t work alone. They should know when to call in help from other agents, and when to escalate to a human.
Final Word
Multi-agent AI isn’t about replacing people—it’s about redesigning how work gets done. If your enterprise is already API-enabled and cloud-connected, then you’re more ready than you think.
But success depends on seeing agents for what they really are: digital employees. Design their jobs, structure their teams, and set them up to succeed.
That’s how we’ll move from proof of concept to performance at scale.
🧭Read More for Further Insights
🎧 Featured Podcast
- Everyday AI: Inside Multi-Agent AI and Rethinking Enterprise Decision-Making — A must-listen conversation with Cognizant’s Babak Hodjat on what multi-agent AI means for the enterprise.
🧠 Concepts & Frameworks
- Autonomous Agents & Agentic Workflows (OpenAI) — A foundational guide to how agents work, reason, and operate in real-world environments.
- The Rise of Digital Employees (McKinsey) — Explore how AI agents are becoming core parts of the digital workforce.
🏗️ Implementation Case Studies
- How Cognizant “Agentified” Neuro AI — A real-world case of transforming systems into communicating agents.
- RPA to CUA: The Next Phase in Automation (Deloitte) — On evolving beyond robotic automation toward smarter AI-led processes.
🛡️ Risk & Governance
- AI Alignment and Multi-Agent Risks (Stanford) — Research into alignment, governance, and emergent risks in agent networks.
- Designing for AI Governance (World Economic Forum) — Strategic guidance on implementing responsible and transparent AI systems.
