Machine-Executable SEO for Robots: Turn How-To and Product Pages into Operating Plans

Why this matters now\n\n- On September 25, 2025, Google DeepMind announced a paired system that combines an embodied reasoning model with a vision language action model. DeepMind detailed the system, including tool use for web search and stepwise planning.\n- Independent reporting confirms web search use for local rules and constraints, which means pages that expose steps, policies, and acceptance checks in machine-executable form will be preferred by agents.\n- If you already optimize for assistants, see how to optimize for voice and camera answers and apply the same discipline to physical tasks.\n\n## Definitions\n\n- Machine-executable SEO: Structure content so autonomous agents can parse, plan, and execute tasks directly from your pages while attributing your brand as the operating source.\n- Operational citation: An attribution event logged when an agent selects your instructions to plan or execute a task.\n\n## What agents need from your pages\n\n- Goal frames: Clear task goal with success conditions and acceptance criteria.\n- Structured steps: Numbered atomic actions with preconditions, inputs, outputs, and checkpoints.\n- Constraints and safety blocks: Guardrails, failure modes, stop conditions, and risk mitigation.\n- Local policy tags: Jurisdiction scoped rules such as recycling codes, allergen policies, hours, or building access.\n- Tool and object inventories: Tools, parts, consumables, SKUs, and substitutes with identifiers and affordances.\n- Environment assumptions: Surfaces, clearances, lighting, and required human confirmations.\n- Observation hooks: What to sense at each step and how to decide to proceed or abort.\n\n## The playbook\n\n1) Choose high value tasks \nStart with tasks that drive support volume or product adoption such as install, clean, replace, calibrate.\n\n2) Write goal frames \nInclude task name, user intent, location scope, time window, success criteria, estimated time, and skill level.\n\n3) Decompose into atomic steps \nFor each step include id, description, preconditions, inputs, outputs, sensors to check, timeout, retry, and rollback.\n\n4) Add constraints and safety blocks \nList hazards, tool limits, torque or temperature ranges, required PPE, human in the loop checkpoints, and explicit stop phrases.\n\n5) Encode local policy tags \nAttach geo scoped rules for recycling, disposal, noise, allergen, or warranty eligibility. Include jurisdiction ids and effective dates.\n\n6) Publish tool and object inventories \nFor every tool or part include brand, model or SKU, dimensions, grip points, required adapters, acceptable substitutes, and where to acquire.\n\n7) Provide observation and validation hooks \nDescribe what successful state looks like in text and images, provide tolerance ranges, and define a quick self check after each major step.\n\n8) Map to existing schema and extend safely \nUse schema.org HowTo, HowToStep, HowToTool, Product, and Offer for compatibility. Put advanced fields in additionalProperty with a machine namespace to avoid breaking consumers.\n\n9) Version and timestamp every procedure \nInclude version, effective date, change log, and deprecation policy so agents can prefer the freshest safe plan.\n\n10) Measure operational citations and outcomes \nLog when agents select your plan, the environment, success or failure code, and downstream conversions. Use these signals to refine steps and safety.\n\n## Minimal machine plan fields to expose\n\n- plan.version: string\n- plan.goal: string\n- plan.successCriteria: list of checks\n- plan.context.location: geo and jurisdiction ids\n- plan.context.environment: surfaces and constraints\n- plan.tools: list of tools with ids and affordances\n- plan.objects: parts and consumables with SKUs and substitutes\n- plan.steps[n]: id, description, preconditions, inputs, outputs, observe, timeout, retry, rollback\n- plan.safety.blocks: hazards, thresholds, stopConditions\n- plan.policy.local: policyName, jurisdiction, rule, citation, effectiveDate\n- plan.validation: final checks and telemetry to record\n\n## Example field mapping for a How To page\n\n- @type: HowTo\n- name: Replace HEPA filter on Model X120\n- supply: HEPA filter SKU HX120, nitrile gloves size M\n- tool: Phillips screwdriver #2, torque limiter 0.4 N·m\n- step[1]: Power down and unplug, precondition mains switch off, observe LED status off\n- step[2]: Remove panel screws, torque max 0.4 N·m, observe panel free of resistance\n- safety.blocks: shock risk, sharp edge warning, require gloves\n- policy.local: disposal rule for used filters with city code and facility list\n- validation: run air quality sensor for 2 minutes, PM2.5 below threshold\n\n## Example field mapping for a product page\n\n- @type: Product with isRelatedTo a HowTo plan and with additionalProperty for grip points, weight, center of mass, surface friction class, and safe cleaning agents.\n\n## Content design tips\n\n- Write step text so it is unambiguous and measurable.\n- Prefer numbers, ranges, and named parts over adjectives.\n- Add step photos or line drawings with callout labels that match text ids.\n- Mark substitutes clearly and state when a human must confirm a risky action.\n\n## Governance\n\n- Security: Block dangerous actions by default and require opt in scopes for anything with heat, chemicals, or cutting.\n- Compliance: Keep policy tags current per jurisdiction. Expire or override when rules change.\n- Observability: Require agents to report plan version, step count, and stop reason back to your endpoint.\n\n## How to win operational citations\n\n- Be the fastest to provide a complete plan with fresh timestamps and clear local tags.\n- Provide safe defaults, explicit stop conditions, and measured success checks.\n- Publish inventories with SKUs and substitutes to reduce task abandonment.\n- Include a short natural language summary for agent ranking plus the structured plan fields.\n- For adjacent assistant traffic, study model-aware AEO for Copilot and apply consistent telemetry.\n\n## What changed with Gemini Robotics ER 1.5\n\n- Agents can call tools such as Google Search to fill missing local rules before acting, which allows policy tagged pages to rank for action selection, not just search ranking.\n- ER planning is paired with a VLA control loop that executes steps and explains the reasoning, which favors pages with explicit checkpoints and validation hooks. See the announcement above and the external coverage linked earlier.\n\n## Success checklist\n\n- Goal frames, atomic steps, constraints, local policy tags, tool and object inventories, observation hooks, versioning, and telemetry endpoints are present and testable.\n\n## Roadmap\n\n- Start with your top 10 support tasks and top 10 product setups. Convert to plans, add local policy tags, publish, and A B test with developer sandboxes that target agent tool use. For distribution beyond search, review Perplexity Comet optimization tactics.\n\n## Bottom line\n\nGemini era agents reward structured instructions that are safe, local, and executable. Structure your pages as operating plans to capture operational citations and convert robotic intent into real world outcomes.