Instead of a developer manually signing up for dozens of APIs and managing keys, an AI agent uses Orthogonal to instantly find and pay for any API it needs on its own.

Orthogonal's core engineering use case centers on its Skill Catalog and unified API gateway, which together enable LLM-powered agents to autonomously discover, authenticate against, and transact with hundreds of APIs through a single interface. Each "skill" is a pre-built, documented integration that encodes authentication flows, parameter schemas, error handling logic, and usage-based billing into a format any agent capable of processing markdown or the Model Context Protocol (MCP) can consume. This eliminates the traditional multi-step developer workflow of reading docs, creating accounts, generating API keys, and configuring billing for each provider. The system supports pay-as-you-go microtransactions, meaning agents can dynamically select the best API for a given task based on cost, latency, or capability — and pay for only what they use. The open-source CLI and SDKs allow agent framework developers to integrate Orthogonal's orchestration layer directly into their agent runtimes, enabling fully autonomous multi-API workflows without human-in-the-loop key management. This architecture is designed to scale as the number of available APIs and active agents grows, creating compounding network effects.

It's like giving your AI agent a universal credit card and a phonebook for every API on the internet, so it never has to ask you to sign up for anything again.

Orthogonal automatically tracks every tiny API call an AI agent makes and handles instant payments to providers, like a smart electricity meter for the API economy.

Orthogonal's operations-focused ML use case revolves around its programmable billing and payment orchestration engine, which manages real-time, usage-based microtransactions between AI agents and API providers at scale. Traditional API billing models — monthly subscriptions, tiered pricing, manual invoicing — break down when autonomous agents make thousands of unpredictable, small-value API calls across dozens of providers in a single workflow. Orthogonal solves this by abstracting billing into a unified layer that meters each API call, calculates cost in real time, and settles payments instantly or in batched micro-settlements. The system likely leverages anomaly detection and predictive models to flag unusual spending patterns (agent runaway costs), optimize prepaid balance management, and forecast demand across the API marketplace. For API providers, Orthogonal offers a new monetization channel with zero onboarding friction — providers list their APIs, set pricing, and receive payments automatically as agents consume their services. The founder team's deep experience in payments (Coinbase) and billing (Vercel) suggests sophisticated fraud detection, spend controls, and compliance tooling are either built or on the near-term roadmap, including potential stablecoin and programmable wallet integrations for global, borderless settlement.

It's like Uber's surge pricing meets your electric bill — every API sip gets metered, priced, and paid for automatically, so neither the agent nor the provider ever has to chase an invoice.

Orthogonal is building a system that verifies AI agents the way banks verify customers — making sure every agent accessing an API is who it claims to be and is authorized to spend.

As autonomous AI agents proliferate and begin transacting independently across hundreds of APIs, a critical security challenge emerges: how do you verify the identity, intent, and authorization of a non-human actor making financial transactions? Orthogonal is positioned to build the definitive agent identity and trust layer — a "Know Your Agent" (KYA) framework — that authenticates agents, assigns trust scores based on behavioral history and provenance, and enforces granular access controls determining which agents can access which APIs at what spending thresholds. Drawing on co-founder Bera's experience building Google reCAPTCHA (one of the world's largest bot-detection systems), the platform likely incorporates behavioral analysis, request pattern fingerprinting, and anomaly detection to distinguish legitimate agent activity from adversarial or compromised agents. This trust layer would also support consent management and audit trails for enterprise compliance, ensuring that every agent transaction is traceable and attributable. Alignment with emerging standards like x402 (HTTP-native payments) and ERC-8004 (on-chain agent identity) suggests Orthogonal is designing for interoperability across both Web2 and Web3 agent ecosystems. This security infrastructure becomes increasingly critical — and increasingly defensible — as the volume and value of agent-to-API transactions scales.

It's like building a TSA PreCheck for AI agents — the trustworthy ones breeze through to any API, while sketchy bots get flagged and grounded before they can cause damage.