Every AI agent today faces the same bottleneck: it must decide which tool to call, then it must actually call it. The first problem gets the attention. The second one is quietly eating teams alive.
A new startup called Conduit, listed on Product Hunt, is building for the second problem. The pitch is simple. Agents accumulate long lists of tools. Each tool is an API endpoint, a database query, a function call. As the list grows, the agent’s context window fills with tool descriptions. Latency increases. Error rates climb. The agent starts calling the wrong tool, or it forgets one exists. Conduit offers a managed routing layer that sits between the LLM and the tools, handling discovery, authentication, rate limiting, and response formatting.
The product targets a specific pain point that has emerged over the past 18 months as agentic workflows moved from demo to production. Early agent prototypes called two or three tools. Production agents routinely call dozens. Companies like Salesforce, Microsoft, and Adept have all described internal agents that reference hundreds of possible actions. Each tool requires documentation, a schema, an authentication flow, and error handling. Teams end up writing custom middleware for every agent. Conduit wants to be that middleware, sold as a service.
Conduit is not the first to spot this gap. LangChain offers toolkits and pre-built integrations. Vercel’s AI SDK includes tool-calling primitives. What distinguishes Conduit is its focus on the operational layer rather than the orchestration layer. It does not replace the agent framework. It replaces the hand-rolled integration code that teams write to connect their agent to their APIs.
The timing matters. The agent ecosystem is entering a phase where the marginal cost of adding a tool to an agent must approach zero for the architecture to scale. If every new tool requires a developer to write a schema, test the endpoint, and configure error handling, agents will never reach the breadth that the industry is promising. Conduit’s bet is that tool integration becomes a commodity infrastructure layer, like DNS or CDN delivery, rather than a competitive differentiator.
The business model is predictable. Conduit charges per tool call, with tiers based on volume. The company’s pricing page lists a free tier for up to 1,000 calls per month and paid plans starting at $29 per month for 10,000 calls. Enterprise pricing is custom. The unit economics depend on whether Conduit can keep its own latency lower than what a team would achieve with a direct API call. Every millisecond of overhead is a reason for a latency-sensitive team to build their own integration instead.
The skeptical view is that Conduit solves a problem that should not exist in the first place. The industry is converging on standardized tool-calling protocols. OpenAI’s function calling, Anthropic’s tool use, and Google’s function declarations all follow similar patterns. As these protocols stabilize, the need for a third-party routing layer may shrink. The counter-argument is that standardization of the call format does not eliminate the operational complexity of managing dozens of live integrations with different authentication schemes, rate limits, and failure modes.
Conduit’s early traction is hard to assess from the Product Hunt listing alone. The product appears to be in beta, with a waitlist for access. The company has not disclosed funding or team size. The Product Hunt page shows a clean demo video and a documentation site, but no published case studies or customer logos. The typical pattern for infrastructure startups at this stage is to land a handful of design partners before scaling sales.
The category Conduit is entering has a known failure mode. Developer tools that solve a real pain point often get absorbed by the platforms they complement. If LangChain, Vercel, or the major LLM providers add tool-routing as a native feature, Conduit’s standalone value proposition weakens. The startup’s survival depends on moving faster than the platforms, or on serving a use case that the platforms neglect.
The most interesting question for AI builders is not whether Conduit succeeds. It is whether the tool-routing layer should be a product at all, or whether it is a natural part of the agent runtime. The answer depends on how the agent stack evolves. If agents become more like operating systems, with a kernel that manages resources and peripherals, then tool routing belongs in the kernel. If agents remain application-layer code that calls external services, then tool routing is middleware, and middleware is a market.
Conduit is betting on the middleware future. The Product Hunt listing is a small signal, but it points to a real shift. The AI industry is moving from building agents that work to building agents that work at scale. That transition creates a new layer of infrastructure, and Conduit wants to own a piece of it.