FC Insights
We have seen that early AI adoption focused on model selection, but the shift has moved toward autonomous, multiple agents for enterprise automation: systems that reason, plan, and execute workflows independently. Unlike static models*, these agents integrate across platforms to trigger real-time actions.
Businesses can either build things themselves, which can take a long time and cost a lot of money, or buy off-the-shelf agentic models that may not be customisable or private. As a result, the market is moving toward "ready-to-use" solutions that hide the technical details by offering pre-configured layers for memory and tool execution. These platforms often have no-code tools, which let teams that aren't technical change agents as needed. This shifts the focus from managing infrastructure to reaching core business goals.
Businesses can either build things themselves, which can take a long time and cost a lot of money, or buy off-the-shelf agentic models that may not be customisable or private. As a result, the market is moving toward "ready-to-use" solutions that hide the technical details by offering pre-configured layers for memory and tool execution. These platforms often have no-code tools, which let teams that aren't technical change agents as needed. This shifts the focus from managing infrastructure to reaching core business goals.
*The model that remains fixed after the initial training, does not adapt according to needs
Enterprise AI Agents: Can Companies Build Their Own?
The appeal of building a proprietary AI agent is clear. Proprietary systems offer greater control, stronger security, and the kind of deep customization that off-the-shelf tools rarely match. However, the execution is rarely straightforward. The recent data shows that about 20% of enterprises are building their own custom agents from scratch using APIs and open-source models, 21% rely on pre-built agents, while 47% use a hybrid approach.
While some firms can build custom agents with relative ease, the majority of enterprises still lack the internal capabilities to do so. The challenge relies on the complex architectural infrastructure and a lengthy AI agent development lifecycle. Even when firms possess the capacity to build them, the ongoing maintenance and associated costs present additional hurdles.
What Comprises the Multilayer Infrastructure?
An effective agent for enterprise-level is rarely a single piece of software, but a vertical stack consisting of multiple layers:
Why is Building an AI Agent an Infinity Loop?
The complexity of enterprise AI extends beyond the initial architecture layer; it lies in the never-ending requirement for every layer to have its own specialized development lifecycle from planning to maintenance. This creates an infinite loop where:
With all these technicalities, what solution actually allows an enterprise to build and scale its AI agents as the business moves?
One-Stop, No-Code AI Agent Builder
One of the strategic paths is to buy a solution of end-to-end agent builders that offer enterprises strategic benefits. They handle the difficulties of multi-agent production from development and maintenance with the benefits of in-house-built systems, such as customized workflows and robust security.
Not to mention, we see rising startups in this space that start from open-source foundations. By utilizing smaller architectures, they can deliver 60% higher accuracy at 10 to 100 times lower cost than large, closed systems, which remain relevant for new adopters.
Not to mention, we see rising startups in this space that start from open-source foundations. By utilizing smaller architectures, they can deliver 60% higher accuracy at 10 to 100 times lower cost than large, closed systems, which remain relevant for new adopters.
No-code platforms further remove the last friction point: deployment complexity. With efficiency gains of 50% or more, teams can build what the business actually needs, with no technical barrier. The downstream effect is a rebalancing of priorities, away from building and maintaining infrastructure and toward scaling business.
Source: Orbislabs.ai, Bain & Company, IBM, Glean, Riseup Labs, KPMG, *O'Neill et al., "Fine-Tuning Small Open-Source LLMs to Outperform Large Closed-Source Models by 60% on Specialized Tasks," Parsed x Together AI, FC Team Analysis