Learn the system
Learn the layers between AI-readable content and an agent that can safely complete a task through APIs, feeds, or protocols.
Build the mental model
Stage 08 · Improve
Move beyond being readable in an answer. Design information, interfaces, permissions, and transactions so authorized agents can discover and safely act.
Outcome
Evaluate agent discoverability, machine-readable capabilities, authentication, permissions, state changes, and human oversight.
Learn → Do → Prove
Learn the layers between AI-readable content and an agent that can safely complete a task through APIs, feeds, or protocols.
Build the mental model
Map one customer job from discovery to action, including data contracts, permissions, failure states, and human approval.
Create the working artifact
Demonstrate a bounded machine-readable path or prototype and document its security and operational limits.
Check the evidence
Concept boundaries
A clearly described action, input, output, and constraint that software can discover and interpret.
Publishing a description does not make the underlying action safe or executable.
A task path in which an authorized agent discovers information, makes bounded choices, and may request or perform an action.
Autonomy should not exceed the identity, permission, and approval model.
An operation that creates, updates, purchases, books, sends, deletes, or otherwise changes a system or external commitment.
Machine readability never removes the need for authentication, authorization, validation, idempotency, and recovery.
Core lesson
An agent may understand a page yet have no safe path to complete the user's job.
Map the layers: discoverable information, structured facts, capability description, interface contract, authentication, authorization, confirmation, state change, receipt, and recovery. A gap at any layer changes what the journey can responsibly promise.
Static files and metadata can improve discovery. APIs, feeds, tools, or protocols can expose capability. Neither should imply permission to act on behalf of a person without the required controls.
A useful interface declares what happens when data is missing, stale, duplicated, unauthorized, or partially completed.
Define inputs, validation, permissions, side effects, idempotency, rate limits, error states, confirmation language, receipts, and escalation. Treat observability and audit logs as part of the capability, not an afterthought.
Begin with read-only or reversible paths where possible. Expand autonomy only when the evidence shows the team can detect failure, contain impact, and restore a safe state.
Decision framework
How far can this journey safely progress today?
Can an agent find the correct facts and capability description?
Fix canonical information and machine-readable discovery first.
Are inputs, outputs, constraints, and freshness explicit?
Define the contract and validation before execution.
Can identity, scope, and user intent be verified?
Keep the path read-only or require human handoff until permissions are sound.
Can the system prevent duplicates, record the result, and recover from failure?
Permit bounded state change only with confirmation, idempotency, audit, and recovery.
Worked non-client example
A service company wants an AI agent to book consultations directly from a public capability page.
Expose a read-only availability check and a human-confirmed booking handoff before allowing autonomous booking.
Discovery is ready, but the state-changing contract and recovery path are not.
A prototype can prove the bounded path works under test conditions; it does not establish production security or universal agent compatibility.
Reusable work template
Map one customer job from discovery through recovery.
State the intended outcome, actor, and who may authorize each step.
List canonical sources, freshness, and machine-readable representations.
Define action, inputs, outputs, constraints, validation, and side effects.
Specify identity, scope, least privilege, and human approval points.
List duplicate, timeout, partial, denied, stale, and cancellation paths.
Define logs, receipts, monitoring, owner, and production-readiness boundary.
Failure modes and corrections
A file or schema is presented as proof that agents can complete the task.
Description does not provide an authenticated, reliable execution path.
Separate discovery evidence from executable capability evidence.
The flow handles success but not duplicates, partial completion, denial, or cancellation.
A state-changing failure can create real commitments or data inconsistency.
Design idempotency, receipts, recovery, and human escalation before autonomy.
Because an endpoint is reachable, the agent is assumed to have authority to use it.
Reachability does not establish identity, scope, or user intent.
Require explicit authentication, authorization, and confirmation boundaries.
Practice exercise
Choose a real customer job and stop the prototype at the last layer the current controls can support.
Proof artifact
An agent journey map, capability contract, bounded prototype evidence, and readiness backlog.
Completion rubric
ACADEMY KNOWLEDGE LIBRARY
Start with a stage, then use concept and intent signals to choose the right depth.
AI shopping assistants decide whether to mention a product before they decide how to rank it. To enter the shortlist, sellers must first make the product understandable to machines.
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Read articleThe ARIA markup you write for accessibility is actually the cleanest source code fed to the AI engine - the same setting, both sides benefit.
Read articleWhat AI looks at is not the beautiful layout you have in the browser, but the clean part it draws. Three actions to make your HTML better captured by AI.
Read articleCan AI read the product and recommend it to a shopper? This three-layer ecommerce GEO audit connects catalog structure with recommendation rate.
Read articleInstead of perfecting the HTML semantic markup and betting on the model to guess the correct layout, it is better to directly give AI a clean Markdown clone.
Read articleProof task
Demonstrate a bounded machine-readable path or prototype and document its security and operational limits.
Deliverable
An agent journey map and readiness backlog that separates content discoverability from executable capability.
Supporting field library
Use these resources for depth. Some premium whitepapers retain their existing library gate; the core stage remains open.
MCP has become the shared connector across every major AI provider, and WebMCP lets sites expose structured functions for agents to execute tasks directly. This whitepaper explains how the agentic web pushes marketing from "being read" to "being used", and how to start building B2A infrastructure.
From Read to Executed: MCP, B2A, and Agent-Ready InfrastructureConfiguring llms.txt for your site from scratch: syntax, directory structure, how it divides labor with robots.txt, and the common mistakes we've caught on live client sites.
The llms.txt Deployment GuideAfter OpenAI killed Instant Checkout, it pivoted to product discovery, letting merchants submit a feed so products get "fully represented." This guide gives you the format, fields, and refresh spec for the ChatGPT product feed, turning your products into data the AI discovery layer can read and select.
ChatGPT Product Feed Setup Guide: The Data Spec AI PicksThe infrastructure for agent payments landed in 2026: Google opened up AP2, Mastercard Agent Pay shipped, Visa Trusted Agent arrived, and even Visa wired into ChatGPT. This breakdown helps you understand how these protocols differ, which ones merchants should support, and how to make trust signals machine-verifiable.
Agentic Checkout Rails: AP2, Visa & Mastercard DecodedApply the stage with a field tool
Map one customer job from discovery to action, including data contracts, permissions, failure states, and human approval.
Evidence boundary
Use the output for the decision it describes; do not treat a technical scan, self-assessment, or planning model as proof of live AI citations.
APPLY THE LEARNING
Use the linked tool, diagnostic, or service only when its evidence base matches the decision you need to make.