How do we define “AI agents”?

Ask five people to define AI agents and you’ll probably get five slightly different answers. But when we posed the question to our trainers, a few clear themes came up again and again.

At its core, agentic AI represents a shift from reactive systems to goal-driven ones. As Rashmi explains, “It’s the shift from AI that responds to AI that can take initiative and act toward a goal.”

AI agents are dynamic. You give them an objective, and they work out how to achieve it. That sense of initiative came up across responses. Trainers described agents as systems that can: 

• Plan multiple steps and collaborate with other agents

• Make decisions, independently or with other agents

• Use tools like apps, software, and more

• Adjust their approach based on results 

Instead of handling isolated requests, they operate in loops — acting, observing, and adapting along the way.

As Abhishek puts it, it’s a “smart model that’s not just thinking, but trying to get something done”. Saqib adds that this autonomy doesn’t mean a lack of structure. Agents work toward goals within defined boundaries, without needing constant human direction.

“If classic AI is like a really smart calculator or autocomplete, agentic AI is closer to a junior teammate: you give it a goal, and it works through the process with a degree of independence, within defined boundaries and tools,” explained Rashmi. 

TLDR: While the wording varies, our trainers agree on the core idea: AI agents are defined by agency, or the ability to plan, act, evaluate, and adapt. 

Where AI agents shine (and where they fall short)

AI agents aren’t universally good or bad. Their performance depends heavily on how and where they’re used. Our trainers highlighted a clear split between tasks agents handle well and scenarios where they still need support.

Where agents perform best:

• Breaking down complex tasks into clear, multi-step plans

• Automating repetitive or structured workflows

• Analyzing data and spotting patterns

• Collaborating with humans as execution-focused copilots
  
  
  

Where agents fall short:

• Maintaining context over long or evolving workflows

• Handling edge cases that fall outside expected patterns

• Making decisions in high-stakes or high-risk scenarios
  
  
  

Overall confidence reflects that balance. While trainers see strong potential, most remain cautiously optimistic. 

TLDR: AI agents are most effective when they operate within well-defined goals and constraints, with human oversight for judgment calls, edge cases, and risk.

What changes when you train agents, not models

Training a traditional AI model is relatively straightforward: you feed it data, check its outputs, and measure accuracy. Agentic AI flips that script. There’s no “right answer”, but rather an analysis of behavior over time. 

“Training and evaluating agents feels less like grading an exam and more like coaching someone in the real world,” said Abhishek.

Agents act, observe consequences, and adapt, making training and evaluation more complex. Even small mistakes early in a workflow can compound. For AI agent training, success is less about “right vs. wrong” and more about competence, reliability, and alignment with human intent across the entire training process.

Human trainers are critical in this process. They act as mentors and guardrails, shaping how agents reason, prioritize goals, and recover from mistakes. As Saqib puts it, they provide the “grounding layer,” aligning autonomous behavior with human values, real-world context, and practical constraints.

“Improving raw model capability can actually worsen agent behavior unless planning, memory, and guardrails scale with it. Training shifts from ‘make it smarter’ to ‘make it reliable under autonomy,’” added Saqib

TLDR: Training agents is less about answers and more about trustworthy behavior over time. It’s a shift from static evaluation to dynamic coaching.

The hype and reality of AI agents 

The topic of Agentic AI is overloaded with hype and misconceptions, but underneath it all lies a layer of reality. Our trainers agreed on one key take: agents are not ready to replace humans end-to-end.

Overhyped claims:

• Fully autonomous agents replacing humans

• Handling messy or ambiguous goals

• Staying consistent over long workflows

• Making high-stakes judgments

“Most agents are fragile without tight constraints. Give them vague goals or messy environments and they can drift, loop, or confidently do the wrong thing,” said Abhishek. 

Underappreciated strengths:

• Clear goals, tools, and memory boundaries

• Human-in-the-loop guidance and escalation

• Feedback loops and stop conditions

• Small models with strong agent design can outperform larger ones

“People focus on flashy demos, but the real bottleneck is knowing when an agent is doing well, drifting, or about to fail. Progress comes less from smarter models and more from better monitoring and human-in-the-loop systems,” said Saqib. 

The shiny headlines about fully autonomous digital employees are fun, but the practical magic comes from good design, human guidance, and reliable evaluation.

TLDR: Trainers emphasized that co-agency is where the real value lies. Agents perform best when humans provide oversight, define boundaries, and intervene when needed. 

What the future holds 

We asked our AI trainers what they think the industry should expect in the next few years. Again, there was a clear consensus: 

• Agentic AI will shift the way humans work with AI

• AI will be able to handle multi-step workflows, follow-ups, and coordination across tools

• Humans will focus on direction, judgment, and oversight

• A key skill will be knowing what to trust an agent with and when to step in

“Humans won’t be out of the loop, but we’ll spend less energy on execution and more on strategy. People who master managing AI behavior, not just using AI tools, will have a serious advantage,” said Rashmi.

TLDR: Agentic AI will transform what we think of “AI” today from simply doing tasks to steering outcomes, with humans guiding, correcting, and approving along the way.