Most brands are still fighting for human clicks while a new layer quietly decides what gets surfaced first. That layer is made of AI agents that route intent, compare options, and execute on behalf of users.
If you want to get discovered by AI agents, you need to treat them as a new type of search engine, with their own ranking signals and marketplaces. In this guide, I will walk through how discovery works, what to track, and how to optimize in practice.
If you want your brand to get discovered by AI agents, think of it as SEO plus APIs plus trust data. Agents rank and select options based on structured metadata, historical performance, cost, and risk, not on pretty landing pages. The main levers are your data structures, how you expose capabilities, and how you perform inside agent marketplaces.
In simple terms, here is what matters most for agent algorithm ranking and visibility inside agent marketplaces:
If you treat this as a new acquisition channel, with its own analytics and playbooks, you will be ahead of most competitors.
When people ask me how agents discover services, they often expect magic. In reality, it is a series of boring but powerful steps that look a lot like search and API marketplaces.
At a high level, the flow looks like this:
A simple way to visualize it:
| Step | What Happens | Who Owns It |
|---|---|---|
| Intent parsing | Turn natural language into structured goals | Orchestrator or assistant |
| Capability match | Query registries, MCP servers, agent cards, marketplaces | Discovery layer |
| Ranking | Score candidates by fit, performance, trust, and cost | Ranking models and rules |
| Selection | Pick one or compose several agents or APIs | Orchestrator |
| Feedback | Log success, latency, cost, and user outcome | Telemetry and monitoring stack |
Modern stacks rely on protocols like Anthropic’s Model Context Protocol (MCP), which standardizes tool and service exposure, and Google’s Agent-to-Agent (A2A) protocol, which uses agent cards and well-known URLs for discovery. On top of that, marketplaces like Fetch.ai’s Agentverse or OpenAI-style agent catalogs provide searchable directories with reviews, tags, and internal ranking models.
For ecommerce and B2A scenarios, this is not abstract infrastructure. It decides whether a shopping agent shows your catalog or a competitor’s. I go deeper into that strategic shift in The Complete Guide to B2A Commerce [Business to Agents]: Preparing Your Ecom Brand for the AI-First Era.
If you want to influence this flow, you need to show up wherever agents look for capabilities. That includes registries, MCP compatible tool lists, your own A2A agent cards, and structured content. I break down the content side in more detail in my guide on Content SEO for agents, where the focus is less on copywriting and more on machine readability.
Once you are discoverable, the next battle is agent algorithm ranking. This is where things start to look more like recommender systems and less like traditional SEO.
Most modern designs blend three signal families:
You can think of it as a performance graph. Frameworks like AgentRank and AgentRank UC extend PageRank-style logic to agents. Instead of hyperlinks, you have interactions and outcomes. If many high-quality agents route tasks to you and those tasks succeed at low cost and latency, your score climbs. If you are called often but produce poor outcomes, your score decays.
A typical feature set for ranking might look like this:
| Signal Group | Example Features | Why It Matters |
|---|---|---|
| Usage | Calls per day, unique callers, repeat callers | Reveals real demand and stickiness |
| Competence | Success rate, error rate, dispute rate | Protects end users and orchestrator quality |
| Efficiency | Median latency, p95 latency, cost per call | Controls user experience and margins |
| Trust | Identity checks, abuse flags, marketplace rating | Reduces risk for platforms and users |
Learning to rank methods, the same family of techniques used by Google and eBay, are a natural fit here. They optimize not just whether you appear, but where you appear in a list. Azure’s agentic retrieval stack, for example, uses hybrid keyword and vector rankers, then re-ranks results using semantic relevance.
In practice, you cannot fully control the ranking model, but you can feed it better evidence. Strong telemetry, clear scoping of what your agent or API does, and clean failure modes all help.
If an orchestrator can predict that you will respond quickly, handle edge cases, and avoid unsafe actions, you become a safe default choice.
This is where most teams fall short. They want to get discovered by AI agents, but their data is locked in messy HTML, ad hoc APIs, or spreadsheets. Agents need structure.
From what I see in current platforms, three categories of structure matter most:
Here is a practical mapping.
| Layer | Format To Aim For | Example Use Case |
|---|---|---|
| Capabilities | MCP tool specs, A2A agent cards, OpenAPI, JSON | “Book delivery slot”, “Price check” |
| Catalog / content | Product feeds, event feeds, FAQ in JSON or schema | Ecommerce, SaaS pricing, support flows |
| Semantics | Vector embeddings, dense representations | Fuzzy matching for similar intents |
Protocols such as MCP define how to declare tools in a consistent JSON structure. A2A uses agent cards exposed at well known paths like /.well-known/agent-card.json. OpenAI’s AgentKit and similar frameworks register connectors and tools with typed metadata, scopes, and security controls.
For ecommerce or B2A, you need to treat this like structured feed optimization. Clean product attributes, consistently typed pricing and availability fields, and event level telemetry all feed into selection quality. I outline this mindset in more detail in my post on Optimization for B2A, where the focus is connecting your data layer to agent friendly surfaces.
If your current stack is mostly CMS pages, the move is to add a parallel layer. Keep the human layer for brand and storytelling. Build a machine layer for agents, with strict contracts, typed fields, and versioned APIs. The better your data structures, the easier it is for agents to reason about you at scale.
You cannot improve what you do not measure. The good news is that visibility in agent marketplaces and routing layers is quantifiable. It just uses slightly different language than classic analytics.
For internal monitoring and external marketplaces, I track five KPI buckets.
| KPI | What It Measures | Why It Matters |
|---|---|---|
| Impressions | How often your agent or endpoint appears as a candidate | Top of funnel visibility |
| Selection rate | Selections divided by impressions | How attractive you look vs peers |
| Task completion rate | Successful outcomes divided by selections | Core reliability and user trust |
| Repeat selection rate | Unique callers that come back over time | Fit and satisfaction |
| Revenue per session | Monetary value per completed agent session | Commercial impact |
On the technical side, I also track latency percentiles, error types, and tool selection quality. These map closely to the evaluation frameworks used in agent research, which often combine task success, cost, and communication quality.
Marketplaces will expose some of these metrics in dashboards. Others you will need to log yourself. For example, an agent registry might show your ranking and rating inside the catalog, but you still need to tag sessions with source identifiers to attribute revenue.
The key is to tie these numbers back to real levers. If selection rate is low, your metadata and positioning likely need work. If task completion is weak, your flows or external dependencies are brittle. If repeat selection is poor, you may be solving the wrong jobs to be done.
In my experience, the teams that win here treat agent channels like performance marketing. They set baselines, run experiments, and review visibility KPIs weekly, not once a quarter.
So how do you turn all this into a concrete plan instead of another theoretical framework on your whiteboard? I like to use a simple checklist that forces teams to touch discovery, ranking, and measurement in one loop.
You can adapt the checklist below to your context.
| Area | High Impact Actions |
|---|---|
| Discovery | Publish agent cards, MCP tools, and docs in public registries |
| Ranking | Improve success rate, latency, and cost; add clear scope and guardrails |
| Data structures | Ship clean feeds and schemas; remove ambiguity and missing fields |
| Content | Align FAQs, help docs, and product descriptions with agent friendly SEO |
| Measurement | Instrument impressions, selections, completion, and revenue per session |
A practical first sprint might look like this:
On the content side, your knowledge base and product copy should reflect the same structure that agents see. That is where Content SEO for agents becomes a strategic companion to your technical work. You want both the narrative and the JSON pointing to the same truths.
If your business already sells to other businesses, this work feeds directly into a broader B2A motion. The brands that learn to speak fluently to agents, not just to humans, will capture a disproportionate share of automated demand in the next few years.
Q: What does it actually mean to get discovered by AI agents?
A: It means your services, products, or APIs show up as viable options when an agent looks for ways to solve a user’s request. Discovery happens through registries, protocols like MCP and A2A, and agent marketplaces that index capabilities, performance, and trust signals.
Q: How is agent algorithm ranking different from classic SEO?
A: In SEO you mostly optimize pages and links. In agent environments you optimize structured data, manifests, and performance logs. Ranking models care about success rate, latency, cost, and risk, along with semantic relevance. It is closer to recommendation systems than to keyword stuffing.
Q: I run a smaller ecommerce brand. Where should I start with agent marketplaces?
A: Start with your data and a single capability. Clean your product feed, then define one clear function, for example stock and delivery checks, and expose it through a protocol or marketplace that supports B2A style integrations. From there, track impressions, selections, and revenue, and expand as you see traction.
AI agents are becoming the new routing layer between demand and supply. They do not care about your latest homepage redesign. They care about structure, performance, and trust. If you want to get discovered by AI agents in a noisy market, you need to treat visibility as a full stack effort, from manifests and feeds to KPIs and iteration.
For brands leaning into B2A, this is not a side project. It touches pricing, ops, and support. I unpack the strategic side of that transition in my article on Optimization for B2A, and the content layer in Content SEO for agents.
My suggestion is simple. Pick one concrete use case, wire it into an agent friendly interface, and start measuring. Once you see real sessions and revenue flow through these channels, it stops being a trend and becomes just another performance channel you know how to grow.
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