Publishing Agent Cards for Discoverable Business AI Agents Safely

In this blog post Publishing Agent Cards for Discoverable Business AI Agents Safely we will explain how Agent Cards make business AI agents easier to find, easier to govern, and safer to use across your organisation.

If your business is starting to build more than one AI agent, you will hit a simple but expensive problem: nobody knows which agent does what.

One team has an agent that summarises customer emails. Another has one that checks invoices. IT has a prototype that creates service desk tickets. Six months later, people are asking the same question: is there already an agent for this, and can we trust it?

That is where Agent Cards come in.

An Agent Card is a small, structured description of an AI agent. Think of it like a business card, product label, and access guide rolled into one. It tells other systems the agent’s name, purpose, skills, endpoint, supported formats, and security requirements.

For business leaders, the point is not the JSON file itself. The point is reducing confusion, duplicated spend, and security risk as AI moves from experiments into real operations.

Why discoverability matters once AI agents leave the lab

Most AI pilots start small. A team builds one agent for one task, usually connected to a limited set of documents or systems.

That is manageable when there are two or three agents. It becomes messy when there are twenty.

Without a standard way to describe and publish agents, businesses often end up with:

• Duplicate agents doing similar work in different departments.
• Unclear ownership when an agent gives the wrong answer.
• Security teams unsure which systems an agent can access.
• Developers hard-coding connections between agents, which creates fragile integrations.
• Business users losing confidence because nobody can explain what is approved and what is experimental.

Agent Cards help solve this by giving each agent a clear, machine-readable profile. Other agents, applications, and orchestration tools can read that profile before deciding whether to use the agent.

This builds on the idea we covered in Building Interoperable AI Agents with A2A and Agent Framework. A2A, short for Agent-to-Agent, is a protocol that lets agents communicate using a shared set of rules instead of custom one-off integrations.

The plain-English version of the technology

The main technology behind Agent Cards is a published JSON document. JSON is a simple text format that software systems use to exchange structured information.

In the A2A model, an AI agent can publish its card at a standard location such as:

https://your-agent-domain.com/.well-known/agent-card.json

The “well-known” path is a standard web convention. It means a client system knows where to look without needing a manual email, spreadsheet, or hidden configuration file.

When another agent wants help, it can retrieve the Agent Card and ask practical questions before making a request:

• What is this agent called?
• Who provides it?
• What tasks can it perform?
• What input does it accept?
• What output does it return?
• Does it support streaming responses for longer tasks?
• Does it require authentication?
• Is this the right agent for the job?

That last question is the business value. The card helps systems route work to the right agent, with the right guardrails, instead of relying on tribal knowledge.

What an Agent Card should include

A useful Agent Card should be clear enough for machines to read and sensible enough for humans to review.

Here is a simplified example:

{
 "name": "Invoice Review Agent",
 "description": "Checks supplier invoices against purchase order rules and flags exceptions for finance review.",
 "version": "1.0.0",
 "provider": {
 "organization": "Example Company Finance"
 },
 "url": "https://agents.example.com/invoice-review/a2a",
 "capabilities": {
 "streaming": true,
 "pushNotifications": false
 },
 "defaultInputModes": ["application/json", "text/plain"],
 "defaultOutputModes": ["application/json"],
 "skills": [
 {
 "id": "check-invoice-compliance",
 "name": "Check invoice compliance",
 "description": "Reviews invoice data against purchase order, supplier, and approval rules.",
 "examples": [
 "Review this invoice and identify any approval exceptions."
 ]
 }
 ],
 "securitySchemes": {
 "oauth2": {
 "type": "oauth2"
 }
 }
}

You do not need every business leader to understand each field. But your governance, security, and development teams should agree on what must be included before an agent is published.

At minimum, CloudProInc recommends documenting the agent’s purpose, owner, version, business process, permitted data types, authentication method, and support contact. If nobody owns the card, nobody truly owns the agent.

The business outcome is less AI sprawl

AI sprawl is what happens when every team builds agents in isolation. It feels fast at first, then becomes costly.

A 150-person company may not think of itself as complex, but it may already run Microsoft 365, Teams, SharePoint, Dynamics, Xero or MYOB, a CRM, a service desk, payroll, and several line-of-business applications.

Now imagine agents being created across all of those systems without a catalogue. Finance builds one invoice agent. Operations builds another. Customer service builds a third that answers billing questions but uses outdated policy documents.

Agent Cards reduce that risk by making approved agents discoverable. A central orchestrator or internal catalogue can list what exists, what is supported, and which agents are safe for production use.

The outcome is practical: less duplicated development, faster reuse, and fewer “mystery bots” connected to business data.

Security should be built into the card, not added later

An Agent Card should never be a public confession of everything your agent can access. It should describe enough for safe discovery without exposing secrets, internal credentials, or sensitive system details.

This matters for Australian organisations working toward Essential 8, the Australian government’s cybersecurity framework that many organisations use to reduce common cyber risks. While Essential 8 was not written specifically for AI agents, its principles still apply: control access, patch systems, restrict risky actions, and keep clear records.

For agent publishing, that means:

• Use strong authentication, such as Microsoft Entra ID, before allowing access to production agents.
• Separate public cards from richer internal cards where sensitive details are only visible after sign-in.
• Do not put API keys, passwords, tokens, or internal network paths in the card.
• Record who owns the agent and who approved it for production.
• Monitor agent calls using Microsoft Defender, Azure logging, or your chosen security platform.
• Review cloud exposure with tools such as Wiz, especially where agents connect to workloads or storage.

This is where AI agent work quickly becomes more than a developer task. It touches identity, security, cloud architecture, compliance, and operational support.

We explored the infrastructure side in Designing Secure AI Agent Infrastructure on Azure. Agent Cards are one more layer in that same operating model: make the agent visible, make its purpose clear, and make its access controlled.

How Agent Cards support better business workflows

The real value appears when agents start working together.

For example, a customer support agent may receive a complaint about a delayed order. Instead of trying to do everything itself, it could discover and call:

• An order status agent to check fulfilment.
• A finance agent to check refund eligibility.
• A policy agent to confirm the approved customer response.
• A service desk agent to create a follow-up task.

Each specialist agent publishes an Agent Card that explains what it does and how to interact with it. The customer support agent can then coordinate the work without needing direct access to every backend system.

That matters because broad access is risky. A better pattern is to give each agent a narrow job, clear permissions, and a published contract. The Agent Card becomes part of that contract.

If your agents also need trusted business knowledge, Agent Cards should work alongside retrieval design. Our post on Azure AI Search Knowledge Sources explains how agents can find approved company information instead of guessing from incomplete context.

A practical publishing checklist

Before publishing Agent Cards across your business, start with a simple internal standard.

1. Name agents like business services. “Invoice Review Agent” is clearer than “FinanceBotV3”.
2. Describe outcomes, not internal code. Explain what the agent helps the business do.
3. List skills carefully. A skill should be a task the agent is approved to perform.
4. Declare supported input and output formats. This prevents mismatched requests and broken workflows.
5. Use versioning. If the agent changes behaviour, the card should reflect that.
6. Require authentication for production agents. Discovery should not mean open access.
7. Keep sensitive details behind authenticated extended cards or internal catalogues.
8. Log usage. You should know which systems or agents called which agent, and when.
9. Review cards as part of change management. Treat the card like production documentation, not an afterthought.

For development teams, this has a similar feel to the discipline described in AGENTS.md and AI coding tools. The theme is the same: AI performs better when you give it clear instructions, boundaries, and context.

Where CloudProInc usually starts

For a 50 to 500 person organisation, we would not start by publishing every agent publicly. We would start with an internal agent register and a small number of high-value production candidates.

A good first project might be an internal IT support agent, HR policy agent, or finance exception agent. These have clear users, measurable outcomes, and manageable risk if designed properly.

From there, we help define the Agent Card pattern, identity model, Azure hosting approach, monitoring, and security controls. As a Microsoft Partner and Wiz Security Integrator based in Melbourne, CloudProInc tends to look at this through both lenses: will it help the business move faster, and can it be operated safely?

The bottom line

Publishing Agent Cards is not about making AI look more impressive. It is about making AI agents easier to find, easier to reuse, and easier to govern.

As businesses move from one-off AI experiments to connected agent workflows, discoverability becomes a control point. If you cannot describe what an agent does, who owns it, and how it should be accessed, it probably is not ready for production.

Agent Cards give you a practical way to bring order to that environment before it becomes expensive and risky.

If you are planning business AI agents and are not sure how to publish them safely, CloudProInc is happy to take a look at your current approach. No hard sell — just a practical conversation about what is ready, what is risky, and where Agent Cards could help.