As AI developer tools race to market, a growing number of companies are discovering that the bottleneck is not the model. It is the operational infrastructure around it.
The AI developer tools market has exploded over the past two years. Hundreds of startups are building frameworks, agents, integrations, and infrastructure layers designed to make AI systems more useful in production environments. Funding has followed, with venture capital firms pouring billions into the space, chasing the next platform layer of the AI stack.
But a quieter problem has emerged behind the funding announcements and product launches. Many of these companies, built by small, engineering-heavy teams moving at breakneck speed, are struggling with something that has nothing to do with their models or code. They are struggling with operations.
The challenge is not new. The pattern of technical founders building exceptional products but stumbling on go-to-market execution, partner management, and organizational scaling has played out across every previous wave of enterprise software. What makes the current AI cycle different is the speed. Problems that took SaaS startups two or three years to encounter are hitting AI startups within months of launch.
The Onboarding Problem No One Planned For
Consider the problem of partner onboarding. For AI developer tools, partnerships with other software platforms are essential. An AI agent framework that cannot integrate with the tools developers already use, including project management platforms, CRMs, databases, and communication tools, has limited practical value. But signing a partnership agreement is only the beginning. The operational work of activating that partnership, ensuring technical integration, aligning go-to-market efforts, and maintaining the relationship over time requires dedicated infrastructure that most early-stage AI companies have not built.
Jitesh Luthra, Head of Operations at Composio, an open-source AI agent tooling platform, has been confronting this problem directly since joining the company in early 2025. Luthra is not a first-time operator. He was previously one of three Co-founders, Ishaan Sethi, Harshil Gurha and Jitesh Luthra, behind PropheSee, a big data Software as a Service (SaaS) platform dedicated towards the marketing analytics space that served clients across four continents before being acquired. He later spent three years building operational infrastructure at Blume Ventures, one of India’s leading early-stage VC firms.
When he arrived at Composio, the platform already supported integrations across more than 250 applications. But the operational machinery needed to bring partners and customers onto the platform efficiently did not yet exist.
“When I came in, the technology was ahead of the operations by a wide margin. The product was strong, the integrations were there, but the process of getting a new partner or startup customer from ‘interested’ to ‘active’ was taking two hours of manual work. For an AI tooling company, that is a contradiction. You are selling automation, but your own onboarding is not automated.”
Luthra redesigned the onboarding process from scratch, reducing the time from two hours to five minutes. He also built the Composio for Startups program, a structured enablement track for early-stage companies adopting the platform, which has contributed more than $100,000 in additional annual recurring revenue. On the quality assurance side, he brought in external agencies and established processes that cut error resolution times by a factor of three.
These are not glamorous metrics. They do not appear in product demos or feature announcements. But for a company scaling its partner network to include enterprises like LaunchDarkly, Databricks, and Yelp, they represent the operational foundation without which growth stalls.
A Problem the VC World Recognized First
The gap between product capability and operational maturity is something the venture capital industry identified years ago, though it approached the problem from the other direction. Rather than building operations inside a single company, some firms began investing in platform functions: dedicated resources to help portfolio companies with exactly the kind of operational challenges that AI startups now face at the individual company level.
Luthra saw this firsthand at Blume Ventures, where he led Market Development. His role involved building repeatable frameworks for ecosystem partnerships and designing non-capital support infrastructure that could be applied across dozens of portfolio companies simultaneously.
“At Blume, I was seeing the same operational gaps across 30 different companies. A startup would raise a round, ship a great product, and then hit a wall trying to activate their first 10 enterprise partnerships. The technical integration would work, but the operational side had no process behind it. It was all ad hoc.”
The frameworks he built there drove measurable results, including onboarding more than 20 ecosystem partners such as Google and AWS, and contributing to revenue pipeline growth for multiple portfolio companies. His collaboration with Agora, the real-time communication platform, led to a joint startup accelerator, and he spoke about the partnership at Agora’s RTE 2020 global virtual conference.
That experience, he says, gave him a different lens for understanding what AI startups need today.
Why AI Companies Are Especially Vulnerable
Several characteristics of the current AI developer tools market make operational gaps particularly costly. First, the market is moving fast enough that the window to establish partnerships and capture developer mindshare is narrow. A company that takes six months to operationalize a partner integration may find that a competitor has already locked in that relationship.
Second, AI tooling companies tend to be engineering-first in ways that even traditional SaaS startups were not. The founding teams are often composed entirely of machine learning engineers and developers, with operations, go-to-market, and partnership functions treated as afterthoughts. This is not a criticism of the founders. It reflects the reality that AI products require deep technical talent to build. But it creates a gap that needs to be filled early, not later.
The contrast with the previous generation of SaaS is instructive. A platform like PropheSee, which tracks a brand and its competitors across digital channels, runs analytics and reports, and allows it to take data-driven decisions, could onboard a client through a relatively straightforward integration with one or two data sources. An AI agent that connects to a customer’s CRM, project management tool, and database simultaneously creates dependencies across multiple platforms. Managing those dependencies, ensuring data quality, handling edge cases, and supporting customers through the integration process requires operational discipline that cannot be improvised.
“In SaaS, you could sometimes get away with figuring out operations in year two or three. In AI tooling, the market will not wait that long. The companies that build operational infrastructure alongside their product, not after it, are the ones that will scale.”
What the Industry Gets Wrong
The prevailing narrative in the AI developer tools space emphasizes technical differentiation: better models, more integrations, faster inference. Luthra does not dismiss the importance of these factors, but he argues that they are necessary conditions, not sufficient ones.
The companies that will win the AI infrastructure layer, he believes, are those that treat operational excellence as a first-class priority rather than a support function. That means investing in partner operations early, building scalable onboarding systems before they are needed at scale, and hiring people who understand both the technology and the business mechanics of enterprise partnerships.
Luthra’s own career reflects that conviction. From co-founding a startup that raised Rs. 3.3 Crores (USD 516K) in angel funding through a co-investment from the Indian Angel Network (IAN) and Stanford Angels and Entrepreneurs India and scaling it to an exit, to building operational systems across a venture portfolio at Blume, to now running operations at an AI infrastructure company, the throughline has been the same: the hardest problems in technology companies are rarely technical.
As the AI developer tools space continues its rapid expansion, that may be the lesson the industry most needs to learn.
