Build something custom

Make your product safe for AI agents to use.

Your customers are starting to ask whether your product works with Claude, ChatGPT, or their own agents. An API that was fine for developers is not fine for an autonomous caller. Particular Labs puts an agent-ready boundary around what you already have: discovery, delegated auth, action policy, approval for consequential actions, and an audit trail.

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What agent-ready means

Discoverable

An agent can find what your product does and how to call it, over MCP for tool use and A2A when another agent needs to delegate work to yours.

Governed

Every capability declares its risk. Reads run. Safe writes run with scopes. Consequential actions wait for a human, and the policy is enforced in code, not in a prompt.

Accountable

Idempotent calls, structured failures, an append-only audit ledger, and usage metering per action. When an agent causes a real effect, you can see who, what, and why.

The runtime path

  1. Agent (Claude, ChatGPT, Codex, or another agent)

    01

  2. Identity and delegation

    02

  3. Capability discovery

    03

  4. Risk and policy gate

    04

  5. Execute, or wait for human approval

    05

  6. Your existing app

    06

  7. Audit, usage, status

    07

Three scopes

Agent surface audit

from $750

You send API docs, the auth model, and one to three agent workflows you want. You get a capability map, an MCP versus A2A call, a read, safe-write, consequential classification, a delegated-auth design, the top risks, and a fixed-price build proposal. Credited toward a build within 30 days.

Agent-ready API

fixed quote

Five to twelve well-shaped capabilities on your existing backend. MCP endpoint, A2A agent card when it earns its place, your tenant auth, least-privilege scopes, risk tiers, approval gate, idempotency, audit ledger, metering, tests, deployment, and a runbook.

Agent operations layer

shaped engagement

For products with long-running jobs, several agent consumers, or customer-facing agent access. Durable task lifecycle, status and cancel semantics, per-tenant policy, quotas, and an audit surface your customers can see.

After a build, an optional retainer covers protocol and dependency maintenance, monitoring, and a bounded allowance for capability changes. It does not turn the studio into your outsourced IT.

Who this is for

Vertical SaaS with an API

Property management, field service, compliance, niche CRM, logistics. Agent access becomes a feature you sell, not a support ticket you dread.

AI products with a brittle execution layer

The agent works in the demo. In production, OAuth per user, over-permissioned tools, duplicate side effects, and no approval model. The studio hardens the execution layer. You keep the brain.

Internal tools that agents need to touch

Bespoke systems behind SSO that Claude or Codex should be able to read from and, carefully, act on. Security review included in the scope.

What the studio will not do

  • Build another agent framework. Your orchestration choice stays yours.
  • Compete with connector marketplaces. Where a commodity connector is better bought, the proposal says so.
  • Sell by tool count. Tool count is a scope limiter, not the value.

Built on what the studio already runs

The same pattern runs inside Particular Labs products: one capability declaration, projected to MCP, approval queues, audit types, and billing events by generic interpreters. lilis runs consequential actions through exactly this approval gate every day.

Send the API docs. You get a straight answer on what is safe to expose.

One message with a link to your docs and the workflow you have in mind. A person replies within a few days with whether an audit makes sense.

Tell us about your product