Do you need this yet?
Usually the answer depends on whether a specific, valuable task requires it. A local practice with a working appointment platform typically needs better use of that platform’s existing integrations, not a custom API. A larger commerce or multi-location business with its own inventory or scheduling data may benefit from a dedicated, documented interface.
- Worth exploring when customers or partners already try to reach your data through software, when the same task is repeated at volume, or when errors in automated requests are costly.
- Not worth it yet when the site itself is not findable or usable, when no concrete task is identified, or when a vendor platform you already use covers the need.
Two pilots, two different jobs
Agent-to-agent readiness prepares an agent or system you operate to communicate with other agents. API readiness makes your information and permitted actions reliably usable by any authorized software. A business can need one, both or neither.
Agent-to-Agent Readiness
A scoped readiness assessment and pilot for businesses that want a customer’s agent to communicate with an agent or system they operate, such as one that answers availability questions or takes structured requests.
- A justified business use case, with the task, the value and the risks written down before any build
- Clearly described capabilities and the specific actions supported, so other agents know what to ask for
- Discovery and communication design using mechanisms that fit your systems
- Identity, authentication and permissions, so only authorized agents can act, and only within scope
- Defined inputs, outputs and error handling for every supported action
- Human approval points for anything sensitive, costly or irreversible
- Audit trails and monitoring of every exchange
- A sandbox demonstration of one useful workflow, end to end
Where a client’s systems call for it, we evaluate open protocols such as the Agent2Agent (A2A) protocol for agent discovery and communication and the Model Context Protocol (MCP) for exposing tools and data to AI applications, checked against their official specifications at the time of the project. No universal registry exists where every business must list an agent.
API Readiness for AI Agents
Making accurate business information and permitted actions reliably usable by software, whether the caller is a customer’s assistant, a partner platform or your own tools.
API and integration inventory
What already exists across your platforms, what it exposes and who uses it.
A current API specification
A machine-readable description, such as an OpenAPI document, where appropriate.
Clear fields and consistent data models
Every field defined once and used the same way everywhere.
Reliable core data
Products, services, locations, prices and availability that match the system of record.
Authentication and scoped access
Credentials limited to the data and actions each caller needs.
Rate limits and useful errors
Limits that protect your systems, and errors that say what went wrong and how to fix it.
Idempotency
Actions that must not be duplicated, such as bookings or orders, stay single when a request is retried.
Versioning and change management
Changes announced and versioned so integrations do not break silently.
Sandbox environment
A safe place for integrators and agents to test without touching real customers or money.
Logging and monitoring
Every request recorded and watched for errors, latency and misuse.
Consent and confirmation
Sensitive or irreversible actions require explicit confirmation from the customer or your staff.
An API alone does not improve search rankings or guarantee AI recommendations. Its value is that authorized software gets correct answers and completes permitted actions reliably.
How a pilot runs
- Define the use case: one task, its value, its risks and who approves it.
- Assess existing systems, data quality, access controls and integrations.
- Design capabilities, permissions, approval points, errors and monitoring.
- Build in a sandbox and demonstrate one workflow end to end.
- Review results against agreed success criteria and decide whether to go further.
Measurement and its limits
We report successful authorized requests against total requests, data accuracy on sampled responses checked against the system of record, median and 95th percentile latency, failures by type, and duplicate actions prevented on retried requests.
Pilot results come from a sandbox and may differ under production load. Moving to production is a separate, explicitly approved step.
Fit and prerequisites
- A named business owner for the task and its risks
- Technical access to the relevant systems, or a developer we can work with
- A system of record for the data involved
- A non-production environment, or agreement to create one
Frequently asked questions
Related services
- Search & AI Growth, how the services fit together
- Agentic AI optimization, making your website usable by assistants
- Marketing automation, connecting the systems behind your leads
- Analytics and strategy, measuring what the integration is worth
Find out how AI tools describe you today
Tell us your site and the problem you are seeing. We will reply with a first view and the next step that fits.
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