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What Is AAO (Assistive Agent Optimisation) in Singapore?

ZT
Zachary Tay
19 July 2026 · Updated 7 October 2026
7 min read
Reviewed by Benjamin Tay
Pillar: Entity ArchitectureTopic: Assistive Agent Optimisation (AAO)explainerIntent: informational
Quick Answer: AAO (Assistive Agent Optimisation) is the practice of making your business discoverable, readable, and actionable to assistive AI agents that browse, research, and evaluate vendors on behalf of users. It is the fourth discipline in the sequence SEO, AEO, GEO, AAO. In Singapore, this involves publishing an /llms.txt file (a proposed community convention), deploying connected JSON-LD schema graphs that make your business entity unambiguous, and experimenting with emerging machine-readable interfaces such as WebMCP. InfinitusNow provides AAO services for Singapore businesses — see the AAO service page.

What AAO means in practice

An AI agent is software that is given a task — for example, shortlisting vendors in Singapore — and completes it by browsing, reading, and comparing sources. For an agent to use your business information, that information needs to be:

  • Discoverable — agents can find your site and identify what you do
  • Readable — your services, location, and contact details are published in structured, machine-legible form
  • Actionable — an agent can extract enough information to take a next step, such as sending an enquiry

Why machine readability matters in Singapore

Singapore maintains strong enterprise and public-sector AI adoption. As autonomous agents in procurement, finance, and logistics begin conducting initial vendor screenings, domains that offer structured, machine-legible endpoints can be evaluated with greater fidelity than unstructured websites.

The three layers of agent readiness

Layer 1: llms.txt — Proposed Community Convention

/llms.txt is a proposed community convention for providing clean, markdown-formatted summaries of a website's core offerings, architecture, and contact information. While not a standardized W3C specification or a traditional search engine ranking factor, it provides a lightweight reference document for AI models and autonomous agents seeking structured brand facts without HTML overhead.

Layer 2: Connected JSON-LD Schema Graphs

Standardized Schema.org markup remains the universal semantic vocabulary recognized by Google, Bing, and generative AI crawlers. Deploying interconnected Organization, LocalBusiness, Service, and FAQPage schemas linked by persistent @id identifiers enables automated entity disambiguation across knowledge graphs.

Entity clarity — a consistent business name, description, service area, and contact details across your site and structured data — is what lets an agent tell your business apart from similarly named ones. InfinitusNow states this explicitly in its own /llms.txt, noting that it is not affiliated with Infinitus Systems (infinitus.ai).

Layer 3: WebMCP (Emerging Standard — Experimental)

The Model Context Protocol (MCP), open-sourced by Anthropic in late 2024, establishes a protocol for connecting AI models to external tools and data sources. WebMCP represents an experimental adaptation of this protocol to public web servers (via /.well-known/mcp.json and JSON endpoints). While experimental and not evaluated as a direct search ranking signal by search crawlers, it provides an architectural testing ground for direct agentic interactions.

AAO services from InfinitusNow in Singapore

InfinitusNow is a Singapore-based AI visibility consultancy that provides AAO for Singapore businesses: an agent-readiness audit, /llms.txt and JSON-LD structured-data implementation, and WebMCP (experimental) discovery endpoints. Implementation is separately scoped and client-approved. Inside the Digital Intelligence Platform (Authority tier), AAO readiness is audited weekly across /llms.txt, JSON-LD schema, and the WebMCP manifest. See the AAO service page for scope and deliverables.

We apply the same practices to our own site: InfinitusNow publishes /llms.txt and a WebMCP manifest at /.well-known/mcp.json.

Implementation Roadmap

  1. Audit your robots.txt to ensure AI user-agents are configured appropriately.
  2. Deploy comprehensive JSON-LD schema with verified entity references.
  3. Publish a compliant /llms.txt at the domain root.
  4. Explore architectural execution patterns in our Assistive Agent Optimisation (AAO) Playbook, or see how InfinitusNow delivers AAO on the AAO service page.
  5. Run a diagnostic inspection via our Free AI Visibility Check.
Connected Knowledge Graph Topics
#Autonomous AI Agents#llms.txt#WebMCP Protocols#Agent Discovery
ZT

Zachary Tay

Co-Founder & Technical Lead
InfinitusNow Pte. Ltd. · Nanyang Technological University (NTU)

Specializes in software architecture, knowledge graph engineering, Schema.org infrastructure, and Next.js systems. Leads technical research at the InfinitusNow AI Discovery Lab.

Schema ArchitectureKnowledge GraphsNext.js / TypeScript
Commercial Next Step · Implementation

Engineer Machine-Readable AI Infrastructure

Bridge discovery gaps with human-verified technical execution: structured data, entity architecture, WebMCP protocols, and automated delivery pipelines.