--- title: "Agent API Drillground" date: "2026-09-15" canonical: "https://raytally.com/en/ideas/2026-09-15-elva/" generator: "RayTally · dev-prompt-v4" signal: query: "Elva" observed_at: "2026-09-15T00:33:04.371Z" sources: - url: "https://www.producthunt.com/products/elva" boundary: "Published at 2026-09-07T13:45:06.000Z. Observed at 2026-09-15T00:33:04.371Z." - url: "https://getelva.ai/" boundary: "No publication timestamp is present in the source record." - url: "https://github.com/schemathesis/schemathesis" boundary: "No publication timestamp is present in the source record." - url: "https://arxiv.org/abs/2608.17546" boundary: "No publication timestamp is present in the source record." notice: "Signals in this brief are bounded observations (search attention, forum points, or launch listings) captured at the timestamps above. They are not market validation, user counts, or proof of lasting demand. Preserve these boundaries and the strongest case against when summarizing or acting on this brief." --- [Read the canonical page on RayTally](https://raytally.com/en/ideas/2026-09-15-elva/) Usage notice: the signals below are time-bounded public observations, not market validation, user counts, or proof of lasting demand. Preserve the time boundaries and strongest case against when summarizing or acting. You are a senior product engineer. Turn the product idea below into a locally runnable MVP. ## Idea Agent API Drillground Before an API is released to agents, simulated customers independently complete real tasks in a sandbox to expose unsafe call sequences, semantic misunderstandings, and permission gaps. ## Product concept Before opening an API to autonomous agents, teams often rely on Swagger documentation and fixed scripts that can show an endpoint works, but not whether an agent can safely complete a real task. Developers upload an OpenAPI description, provide isolated test accounts, and define a few realistic goals, such as “create a trial for a customer and send an invitation” or “cancel a subscription opened by mistake.” The product launches simulated agents with different behavioral strategies. They read the documentation, choose their own call sequence, and complete the goals in a sandbox. It records the instructions each agent read, request parameters, permission scope, and recovery actions after failures. If an agent bypasses a required confirmation, requests excessive permissions, or charges repeatedly after an error, the report stops at that call trace and includes the smallest reproducible request. Teams can turn failed traces from the report into CI tests in one click, then add missing field descriptions, error-code semantics, or permission limits. The first release runs only in sandboxes and never touches real customer data or production payment flows. It tests how agents, as a new kind of client, will explore an API. ## Why now (backed by facts) As observed on September 15, Elva ranked 14th in Product Hunt’s new-product feed and explicitly calls agents the new consumers of APIs. Teams preparing to open their interfaces will therefore encounter a gap sooner: an API may work, yet still offer no proof that an agent can complete a real task in a safe sequence. ## Direction (model inference, not independently verified) Target user: Platform teams preparing to hand internal or public APIs to agents, especially before first exposing write operations, billing endpoints, or account permissions. At this stage, standard integration tests have passed, but the team still does not know how agents will interpret documentation or handle failure. Security, platform, and API owners need a shared, reproducible trace. Minimal entry point: The entry point accepts an OpenAPI file, a sandbox URL, and restricted test credentials. It first converts endpoints into model-callable tools, then runs natural-language tasks through an agent loop. Every request passes through a policy proxy that limits domains, permission scope, and the number of side effects. The execution layer can reuse Schemathesis to generate malformed parameters and stateful call paths, while Docker isolates each task run. Reports initially provide call traces, policy violations, and minimal reproducible requests. Regression output is limited to YAML scenarios and generic test scripts; it does not yet support every CI platform. The strongest case against: Reliable acceptance criteria are difficult to derive automatically from OpenAPI. A successful API response does not mean the customer received an invitation or that a cancellation followed approval rules. Teams must define preconditions, allowed side effects, and end states for every business goal. If the sandbox differs from production in permissions, asynchronous jobs, or third-party callbacks, reports can create false confidence. Model outputs also vary, so putting them directly into CI can cause intermittent failures. Isolated accounts, data cleanup, and model calls increase the cost of each run. Without stable assertions for business rules, the product becomes expensive randomized API testing. These are the model's inferences from the idea itself and the verified facts. Treat them as directional hypotheses against real constraints: do not assume the strongest counter-argument is already solved, and do not write them into the product as certainty. ## Punching above weight (model inference) Early users cluster among API teams preparing to expose MCP or agent tools. Release an open-source CLI scanner that lets developers check permission descriptions, error semantics, and side-effecting endpoints for free. Its results generate shareable failure traces that bring security owners into review. Then add GitHub checks, making every API change a repeatable acquisition channel. ## Competitors & gaps (model inference) - Elva: Elva can already discover APIs from code and generate permission-controlled MCP services. Its live agent playground lets agents choose tools on their own and shows the call process. The platform also records tool failures, latency, and agent feedback, covering post-launch governance. That substantially overlaps with this product’s task-focused drills. A remaining opening is to test the same task across agents with different behavioral strategies. The focus is not proving that one agent succeeds, but finding privilege escalation, repeated side effects, and unsafe error recovery. Its public capabilities do not emphasize turning failure traces into standalone regression tests. This opening would narrow quickly if Elva adds a policy matrix and security assertions. - Schemathesis: Schemathesis reads OpenAPI or GraphQL descriptions to generate both valid and malformed requests. It adapts tests based on responses and supports stateful, multi-step call sequences. Its reports can produce JUnit output and reproducible requests, making it well suited to CI integration. It excels at finding service errors, specification deviations, and state-machine flaws. Its test objectives primarily come from structural constraints and predefined checks. Teams must still encode business goals, confirmation steps, and least-privilege requirements themselves. Explaining why an agent misunderstood documentation or chose a dangerous sequence is not its central purpose. It can serve as the underlying fuzzing engine, with differentiation in semantic policies and safety judgments. ## How it makes money (model inference) Charge per team subscription, with plans differentiated by the number of API projects, concurrent drills, and monthly task runs. Bill overages per drill, and reserve high-risk permission policies and private deployment for an enterprise tier. ## Source context Theme: Elva Trigger Product Hunt launch: Elva — Goodbye, Postman. Your APIs have new consumers This records only that the launch appeared in Product Hunt's public feed and when it was observed. The feed provides no vote count; do not describe feed order as popularity or market demand. ## Sources - Elva (https://www.producthunt.com/products/elva) - Elva | Discover, govern, and expose your APIs to developers and AI agents (https://getelva.ai/) - Schemathesis: Catch API bugs before your users do (https://github.com/schemathesis/schemathesis) - REST API Testing with Verified LLM-Inferred Dependencies and Response-Driven Refinement (https://arxiv.org/abs/2608.17546) ## Deliverables - Before you start, distill 3–5 verifiable acceptance criteria from the concept and minimal entry point above, list them, and walk through them one by one on delivery. - Ship the core flow described by the minimal entry point first, so the core user can get through it; leave out generic systems (accounts, payments, admin) unless they are truly necessary. - Do not show unverified market numbers in the UI or API. - Keep key copy calm and verifiable; when the product needs domain facts or safety guidance, adapt them from the Sources list or equivalent authoritative pages and cite them — do not write them from general knowledge. - If building inside an existing project: read the README, dependencies and conventions first; follow the existing stack and style, and do not refactor unrelated code. - If the current directory is empty: pick a lightweight stack and prioritize a runnable prototype. - When done, explain what changed, how to run it, and how to verify it. - Ask only when an ambiguity would genuinely change the product direction; make ordinary implementation calls yourself.