Human-in-the-Loop Is Out, Agent-in-the-Loop Is In

Global Capability Centres (GCCs) in India are entering a defining moment in their evolution. For years, they operated on human-driven models where people performed the critical steps and technology supported them. This aligns with the traditional Human-in-the-Loop (HITL) approach, where human oversight is essential for AI systems to function safely. 

HITL now feels outdated. Advances in autonomous agents, enterprise-grade AI orchestration, and context-aware models have shifted the landscape. Among leading GCCs, Agent-in-the-Loop (AITL) is replacing HITL. 

At the MachineCon GCC Summit 2025 held in Goa from Nov 29 to Dec 1, Jaywant Deshpande, Chief Solutions & Innovation Officer, Accion Labs, said, “This shift is not simply about replacing humans. It is about re-architecting how enterprises operate.”

As GCCs push for higher productivity and exponential scalability, they are discovering the limitations of human-dependent processes and the power of autonomous AI agents that don’t just assist but deliver end-to-end outcomes.

Why HITL Is No Longer Sustainable

The traditional HITL framework assumes that humans must check, validate, and correct the work performed by AI. This made sense when AI models were error-prone or lacked context. But in high-velocity GCC operations, where millions of transactions, tickets, lines of code, or data points flow every hour, humans appear to be the bottleneck.

Validation queues increase, QA cycles slow down automation, and the cost of human oversight rises linearly while AI output scales exponentially. Leaders across the ecosystem are observing the same phenomenon.

“We found that task-specific agents are better, faster, and 1000x cheaper than humans,” Jaywant mentioned.

This is not about replacing people. It is about repositioning them where their strengths matter most. Agents excel at repetitive, rule-driven work. Humans excel at strategy, judgment, and innovation.

As GCCs move towards AI-native operating models, keeping humans in every loop becomes inefficient, expensive, and unsustainable.

Task-Specific Agents

Unlike broad copilots, task-specific agents are tightly trained systems built for one
job: code refactoring, test generation, compliance checks, document extraction, or infrastructure provisioning.

With specialized design, supported by validation agents and structured exception handling, they achieve extraordinary precision and speed.

“We migrated 2.3 million lines of code in under four months, 99% untouched by humans,” Jaywant added.

This level of automation was unthinkable a few years ago, yet it is already standard in advanced GCCs.

In the AITL model:

• Agents execute work
• Agents validate output
• Only exceptions escalate to humans

By automating both task execution and quality control, GCCs are achieving throughputs that previously required hundreds or thousands of employees in HITL environments.

From Humans Checking AI to AI Checking Humans

The most profound shift in enterprise work is a reversal of responsibility. Instead of humans validating machine output, future GCC operating models will use AI to validate human work.

This change marks the arrival of the AITL era, where agents now handle 90%-99% of tasks, provide contextual recommendations, and flag anomalies only when human intervention is required.

“Humans transition from being the principal executors to becoming orchestrators and supervisors. This unlocks a workforce that is smaller but significantly more strategic. Only the GCCs that drive AI and innovation are going to succeed,” said Mandar Garge, SVP & Global Head of Strategy Consulting & Enterprise Transformation, Accion Labs.

Humans remain essential, but not for repetitive work, which often leads to fatigue, inconsistency, and cognitive overload. 

Agents deliver:

• perfect repeatability
• real-time response
• zero deviation
• continuous execution
• instant scale

This shift is not just a marginal efficiency gain but an exponential leap in capability. 

For GCCs, the question is no longer whether agents will replace task-driven workflows, but how quickly they can be deployed. 

In AITL models, humans focus on system design, policy setting, exception handling, strategic oversight, and innovation—areas where human talent creates real value, particularly in AI-first GCCs.

What GCCs Must Do to Prepare for Agent-Led Operations 

A transformation of this magnitude requires more than technology. It demands new thinking, new architecture, and new operating models. 

The most advanced GCCs are moving quickly on three fronts:

1. Automating with agents, not copilots
   Copilots assist individuals. Agents transform entire workflows.
2. Building context layers and Knowledge Graphs, not just data lakes
   Data alone is opaque to agents. Context enables accuracy, autonomy, and safety.
3. Designing AI systems with an engineering discipline
   A shared enterprise context to ensure the architecture stays reliable and scalable.

When Not to Use AI 

“As far as possible, don’t use AI. Use code. Only use AI where nothing else works,” Jaywant added.

This mindset ensures that AI is applied where it delivers disproportionate value, rather than adding unnecessary complexity. 

The GCC of the future will not be defined by real estate, scale, or headcount. It will be defined by AI maturity, particularly the ability to deploy autonomous agents that deliver outcomes at superhuman speed. HITL served the last decade of automation. AITL will define the next.

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