I think the honeymoon period of Agentic AI is over.
For the last two years, the narrative has been simple: AI agents will replace developers, analysts, support teams, project managers, and eventually entire software companies. Every demo looked magical. Every conference promised autonomous businesses. Every startup pitch deck had an agent somewhere in it.
Reality is now arriving.
As someone running an Indian software company, I am beginning to see a very different picture.
The first problem is economics.
A skilled developer, tester, or support engineer is still surprisingly cost-effective. Their cost is predictable. Their output can be measured. Their availability can be planned. Their knowledge compounds over time.
AI usage, on the other hand, remains difficult to forecast.
One month you spend a few dollars. The next month your usage explodes because of context windows, reasoning models, agents calling tools, vector databases, API requests, and multiple subscriptions. Suddenly you are paying for ChatGPT, Claude, Gemini, Cursor, GitHub Copilot, Kilo,OpenCode, and a dozen supporting services just to recreate what one competent engineer was already doing.
The second problem is accountability.
When a human makes a mistake, I know who owns it.
When an AI agent makes a mistake, who owns it?
The model?
The framework?
The prompt?
The tool provider?
The engineer who built the workflow?
The person who approved the output?
In practice, ownership always falls back to a human.
The third problem is reliability.
Agentic workflows work beautifully in controlled demos. Real businesses are messy.
Requirements change.
Customers contradict themselves.
Data is incomplete.
Approvals are delayed.
Exceptions become the norm.
The more unknowns in a process, the more human judgment becomes valuable.
This is especially true in services businesses where the actual challenge is not coding. It is understanding ambiguous requirements, managing stakeholders, negotiating trade-offs, and making decisions under uncertainty.
AI is good at execution.
Business is mostly uncertainty.
The fourth problem is subscription fragmentation.
No single AI product today satisfies all the needs of a serious developer or technical leader.
You need one model for coding.
Another for writing.
Another for research.
Another for long-context analysis.
Another for image generation.
Each has usage limits.
Each has different strengths.
Each changes pricing unexpectedly.
The result is operational complexity that many people underestimate.
None of this means AI is a failure.
Far from it.
AI is already the most important productivity tool introduced during my career.
But I increasingly view it as a force multiplier rather than a replacement.
The strongest teams are not replacing people with AI.
They are making good people significantly more productive using AI.
The difference is important.
One mindset asks:
"How do we remove humans?"
The other asks:
"How do we increase the output of capable humans?"
The second question is producing far better results.
Could this change?
Absolutely.
A major breakthrough in reasoning efficiency, model costs, memory systems, or autonomous reliability could completely alter the equation.
But as of today, especially in the Indian IT services context, human talent remains more accountable, more predictable, and often more economical than fully agentic systems.
The future may still belong to AI.
The present still belongs to teams that know how to use AI without becoming dependent on it.
Technology history is full of overestimating short-term disruption and underestimating long-term impact.
Agentic AI may be following the same pattern.
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