What happens after the AI hype wears off?

EDITOR’S NOTE

Three sessions, one underlying tension

Dear Nanobits Readers,

Last Saturday, I walked into an eChai Ventures meetup expecting three separate conversations. A founders meet. A few fireside chats. Some networking over coffee. The usual weekend rhythm.

One session on AI agents in growth and customer support. One on how builders are actually putting agents into production. One on how investors are thinking about AI, SaaS, and deeptech right now.

In practice, it felt like one conversation unfolding in layers.

The first layer stayed close to customers. What happens when AI stops living in demos and starts replying to real users. What breaks first. What earns trust. What gets rejected quickly.

The second layer moved under the hood. How agents are designed, constrained, evaluated, and improved over time. Why prompting is only a small part of the work. Why most failures have little to do with models and a lot to do with systems.

The third layer zoomed out to capital and market reality. What investors now look for once AI capability becomes widely available. Why labor arbitrage alone no longer works for enterprise AI. Where India still has structural advantages, and where it does not.

This edition of Nanobits is a guided walk through those three layers:

Detailed session summaries from builders and investors working at the edge of production AI. Aniket Bajpai from LimeChat on where agents succeed in customer-facing roles. Pramod Kalipatnapu from Revefi on the engineering discipline required to ship agents that work. Vardhan Dharnidharka from Stellaris Venture Partners on how money thinks about AI companies now.

If you are a founder building AI products who need clarity on what actually works, an engineer shipping agents into production who want practical frameworks, or a business leader evaluating AI investments who needs honest assessments of value, this newsletter is for you. 

The sessions did not promise shortcuts. They offered something more useful: clarity on what works, what does not, and why the next phase of AI will look far less flashy and far more consequential.

The same questions echoed across all three conversations. Where does AI belong inside real work? Who trusts it enough to rely on it? And what changes once AI stops being a demo and starts owning a workflow?

Agents meet customers, and the demo breaks fast

The first session set the tone for the entire evening by grounding AI in the messiest place possible: real customers.

Aniket Bajpai, Founder of LimeChat, sat with Somya Sinha to discuss where AI agents actually work in customer-facing roles. LimeChat builds AI systems for customer support and growth, focused on understanding brand conversations at scale.

His core point landed early and stayed consistent. AI agents work only when they stay narrow. Not narrow in ambition, but narrow in responsibility.

Generic chatbots that try to answer everything tend to disappoint. They miss context, drift in tone, and fail under edge cases. Agents designed for specific workflows behave very differently. When an agent is trained on real customer conversations, scoped to a defined task, and aligned to a brand’s voice, it earns trust quickly.

India makes this distinction impossible to ignore. Aniket described Indian customers as a forcing function. They expect fast responses. They switch platforms without hesitation. They move across languages and channels in the same conversation. 

Those constraints force better design. Building for demanding users creates products robust enough to work anywhere. Serving them well forces systems to handle complexity from day one.

Growth and support teams feel the impact immediately. These functions sit close to revenue and even closer to customer memory. A single bad interaction lingers. A good one compounds.

Somya pushed the conversation into a tension many teams feel but rarely articulate. Engineers often feel more productive using AI tools. Business teams often feel underwhelmed. The reason is simple. Engineers adapt tools to fit their workflows. Business users judge AI by outcomes.

Trust appears only when an entire slice of work disappears. Not a draft. Not a suggestion. Not a reply that needs fixing.

Aniket gave a concrete example from social media workflows. The mechanical parts can be automated. Even the first draft copy can be automated. That is when value becomes obvious to non-technical teams.

Key takeaways:

For business teams: Narrow agents outperform broad ones. Generic chatbots attempting to handle any query tend to fail. Agents designed for specific brand workflows, trained on actual customer data, and scoped to well-defined tasks perform far better. Success comes from depth, not breadth.

For technical teams: Workflow integration matters more than model choice. A simpler model with tight process integration beats a sophisticated model bolted onto existing systems. The wins come from removing manual work completely, not adding chat interfaces to dashboards.

The discipline required: Start with repeatable workflows where agents can own the entire process. Learn what good responses look like for your brand. Know when to escalate. Build evaluation loops, not just launch systems.

This was the first hinge of the day. AI starts as a product story. It becomes a workflow story once it ships.

And once it owns a workflow, the next question follows fast.

What does it actually take to build these agents right?

Agents meet systems, and the prompt stops being the main event

This session pulled the conversation under the hood.

Pramod Kalipatnapu, Founder of Revefi, spoke with Piyush Vijay about what makes agents work in production. Revefi builds AI systems for data operations, focused on cloud cost anomalies and infrastructure monitoring.

Pramod spoke about agents not as chat interfaces, but as systems that do work. He contrasted traditional software, built around fixed flows and dashboards, with agent-driven systems that adapt to user intent and take action.

The strongest use cases are repetitive, high-effort workflows that people perform again and again. At Revefi, each cloud cost anomaly triggers the same investigation cycle: detect the spike, trace the cause, assess impact, and recommend fixes. That predictable repetition makes it perfect for agents.

This led to the most practical mental model of the afternoon: Think of an agent like a junior teammate.

Dump everything on them at once, and they fail. Give them structure, clear responsibilities, and limited permissions, and they succeed.

This framing shifts how teams build. Prompting matters, but orchestration matters more. Memory management, tool access, feedback loops, and evaluation decide whether an agent works in production.

Pramod was clear that many failures have little to do with models. They come from unclear boundaries. Agents with broad permissions and vague responsibilities break trust quickly.

Safety entered the discussion not as policy, but as design. The risk is not intelligence. The risk is access. Databases. APIs. Actions. Context windows tempt teams to pass everything in. That rarely helps.

Pramod echoed what Aniket had said earlier from a customer-facing angle. Agents cannot be static. They need constant testing, learning from usage, and refinement. Treating deployment as a one-time launch guarantees decay.

Defensibility emerged as a quiet theme here. Startups cannot win by breadth. Large platforms already own that game. They win by going deep into one painful problem, understanding it better than anyone else, and building tight systems around it.

That depth is hard to copy.

This naturally led into the final session, which asked a different question.

If agents are systems, and systems touch outcomes, how does money think about them?

Key takeaways:

For business teams: Look for work that repeats with minor variation. If a workflow has clear boundaries and known inputs/outputs, an agent can own most of it. The value becomes visible when automation removes mechanical work completely.

For technical teams: Orchestration beats prompting. Memory, context, tool access, and feedback loops determine whether agents work in production. Most failures come from unclear boundaries. Safety is about constraints, not compliance theater. The risk is giving agents too much access, not the models themselves.

The discipline required: Start with a very clear problem, not a vague goal. Build evaluation frameworks and continuous testing. Treat agent deployment as an ongoing process, not a one-time launch. Static systems fail over time.

Agents meet money, and trust becomes the product

This session reframed everything that came before it.

Vardhan Dharnidharka, Investor at Stellaris Venture Partners, spoke with Sushil Kumar about how capital thinks about AI companies. Stellaris backs early-stage tech startups in India across SaaS, deeptech, and consumer categories.

Vardhan described enterprises moving from experimentation to real decisions, but slowly. Excitement leads to pilots. Pilots stall. Enterprises worry about risk, compliance, and accountability. No one wants to lose their job over unpredictable system behavior.

What has changed is clarity. Buyers now understand where AI fits into workflows, not as magic, but as a specific tool solving concrete problems.

This clarity reshapes the old India SaaS playbook. Labor arbitrage alone no longer works for enterprise AI. Build cheaper in India, sell software that replaces existing tools in the US. That model is harder now. Buyers are not just replacing software. They are trusting systems that affect pricing, decisions, and business outcomes. That requires proximity, trust, and deep problem understanding.

Vardhan pointed to areas where India still holds structural advantages. Consumer products. Fintech infrastructure. High-volume transactional markets. He described examples from recruiting and financial services, where large teams repeat the same work daily.

If an enterprise can reach only ten percent of its base, an agent that runs outreach work changes the math.

The investment lens here was blunt. Model access is widespread. Technology is commoditized fast.

Capital flows to teams that show depth, not demos. Pricing power, trust, and workflow ownership matter more than model choice.

Categories like “AI-first” or “SaaS-first” miss the point. Strong companies start with problems. AI becomes part of the substrate over time.

Key takeaways:

For business teams: Narrow agents win in the market for the same reason they win in product. They create predictable outcomes. Trust and workflow ownership matter more than technology sophistication.

For founders: Technology alone creates no moat. Access to models is widespread. Insight into real pain points is rare. That gap determines which companies attract capital. Start with problems, not technologies. Go deep into one painful use case.

The discipline required: Pricing power, trust, and workflow ownership matter far more than model choice. Shallow problem discovery kills more AI startups than technical limitations.

The three sessions converged in the end. Narrow agents win. Systems beat prompts. Trust beats speed. Learning beats static sophistication.

END NOTE: NANOBIT’S TAKE

The three conversations never used the word "hype," but they spent two hours dismantling it.

What stood out was not optimism or skepticism. It was specificity. The room talked about agents that resolve customer queries in brand voice, systems that investigate cloud cost spikes, and enterprises that can only reach 10 percent of their target audience. That grounding in real work made the afternoon feel different from most AI discussions.

One thread connects everything: learning speed matters more than starting knowledge. Pramod described AI as compressing feedback cycles that used to take days into minutes. Aniket spoke about building, observing customer usage, and feeding that learning back into systems. Vardhan pointed at adaptation as the real competitive edge. The teams winning are not those with the fanciest models. They are the ones learning fastest from production data.

This has implications for builders in India. The old advantages around cost arbitrage are fading for enterprise AI. But new advantages are emerging. High complexity customer environments force better design. Consumer and high-volume transactional markets offer strong unit economics. The ability to build for demanding users creates products that travel well globally.

The discipline required is clear. Start with a problem, not a technology. Go narrow, not broad. Integrate deeply into workflows, not superficially into interfaces. Build evaluation loops, not just demos. Ship with boundaries, not limitless permissions. Earn trust through outcomes, not promises.

AI will disappear into software the same way databases and APIs did. Long-term winners will not sell AI. They will sell results. Customer support that actually resolves issues. Data systems that catch anomalies before they cost money. Outreach that reaches more people without hiring more humans.

The shift from experimentation to production is happening now. The companies that survive this phase will be the ones that understood one thing clearly: the technology is commodity, the problem understanding is moat, and the workflow ownership is product.

That clarity showed up in every conversation at the meetup. It is worth paying attention to.