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Most AI assistants in this space know about APIs. How they work in general, what good design looks like in theory.

That's not what these two do.

𝗧𝗵𝗲 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗘𝘅𝗽𝗲𝗿𝘁 𝗸𝗻𝗼𝘄𝘀 𝘆𝗼𝘂𝗿 𝗰𝗮𝘁𝗮𝗹𝗼𝗴. Ask it what's available, what use cases are already covered, who manages what. Useful when a developer is about to build something that already exists. Also useful when an agent needs to find the right API before it does anything.

𝗧𝗵𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 𝗘𝘅𝗽𝗲𝗿𝘁 𝘄𝗼𝗿𝗸𝘀 𝗲𝗮𝗿𝗹𝗶𝗲𝗿 𝗶𝗻 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀, during the spec phase. Generate a draft from a design intent, fix a definition before the error propagates, fill a gap before an agent encounters it. All of it aligned to your organization's specific standards and policies, not a generic checklist.
Both are in ApiShare 2.0.

→ https://lnkd.in/eFwb4NH3

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𝗪𝗲 𝗸𝗲𝗲𝗽 𝗮𝘀𝗸𝗶𝗻𝗴 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗼𝘂𝗿 𝗔𝗣𝗜𝘀 𝘄𝗼𝗿𝗸.
The better question: 𝗮𝗿𝗲 𝘁𝗵𝗲𝘆 𝗴𝗼𝗼𝗱 𝗲𝗻𝗼𝘂𝗴𝗵 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 𝗼𝗻?

A developer figures things out. They read between the lines, check the docs, ask a colleague. When you instruct an agent to use an API, you don't have that buffer. You define the behavior upfront, in code. If the spec has gaps, those gaps become assumptions baked into the agent.

Sometimes the assumptions hold. Sometimes they don't. And when the API changes, you start over.
Swipe through the FAQ to see what agents actually need from an API.
→ https://lnkd.in/eFwb4NH3

hashtag#APIDesign hashtag#AgenticAI hashtag#APIGovernance hashtag#MCPServer hashtag#ApiShare

𝗮𝗽𝗶𝗱𝗮𝘆𝘀 𝗔𝗺𝘀𝘁𝗲𝗿𝗱𝗮𝗺. 𝗧𝘄𝗼 𝗱𝗮𝘆𝘀 𝗼𝗻 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀.

The theme this year is Sovereign Intelligence — how APIs become the bridge between AI regulation and accountable, machine-readable services. Public sector, private sector, same question: how do you make AI work without losing control of what it does?
That question sits exactly where we work every day.

Biagio Zampogna is there representing ApiShare. 📍

Governance doesn't start when something goes wrong. It starts here.

hashtag#apidays hashtag#APIGovernance hashtag#AgenticAI hashtag#SovereignIntelligence hashtag#ApiShare

𝗠𝗼𝘀𝘁 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗴𝗼𝘃𝗲𝗿𝗻 𝘁𝗵𝗲𝗶𝗿 𝗔𝗣𝗜𝘀 𝗮𝗳𝘁𝗲𝗿 𝘁𝗵𝗲 𝗳𝗮𝗰𝘁.
They publish, then align. Release, then document. Deploy, then fix what doesn't match the standard. With human developers consuming those APIs, this works. Badly, but it works. Developers adapt. They ask questions. They work around gaps.

𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗱𝗼𝗻'𝘁.

An agent uses what it finds, exactly as it is. A vague parameter description isn't a minor annoyance. It's an input to behavior nobody designed. An undocumented edge case isn't a known issue. It's a variable that produces results nobody can fully explain after the fact.

The window for correction collapses. The cost of getting it wrong upstream goes up.
Governance by design means the standards are where the decisions happen, in the design workspace, before the product is published. Not in an excel that gets read once and forgotten.

That's what the article we published this week is about.
👉 https://lnkd.in/eFwb4NH3

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𝗠𝗼𝘀𝘁 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗱𝗼𝗻'𝘁 𝘀𝘁𝗮𝗿𝘁 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵. They already have APIs. Dozens, sometimes hundreds. Built at different times, by different teams, with different conventions, or none at all. They respond to requests, but they don't follow the same patterns, they're not documented consistently, and very few were designed expecting AI agents to invoke them autonomously.

Retrofitting governance onto an existing ecosystem is harder than building it in from the start. 𝗜𝘁'𝘀 𝗮𝗹𝘀𝗼 𝘄𝗵𝗲𝗿𝗲 𝗺𝗼𝘀𝘁 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗮𝗿𝗲.

𝗢𝘂𝗿 𝗔𝗣𝗜 𝗗𝗲𝘀𝗶𝗴𝗻 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝘀𝘁𝗮𝗿𝘁𝘀 𝗳𝗿𝗼𝗺 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗵𝗮𝘃𝗲. We audit the ecosystem, identify where standards and consistency break down, and redesign toward something coherent — something that works for developers today and for automated systems that consume specifications exactly as they are.

𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝘀𝗲𝗿𝘃𝗶𝗰𝗲: https://lnkd.in/emRE_4vg

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MCP Servers are getting a lot of attention. Most of it is about implementation: how to configure them, which libraries to use, how to expose them. Very little of it is about what happens when an agent actually starts using them.

An MCP Server exposes enterprise capabilities (functions, data, workflows) in a form agents can discover and invoke dynamically. Not every interaction is orchestrated step-by-step by a developer anymore.

The agent uses what it finds. As-is.

Incomplete specs aren't friction anymore. They're inputs to behaviors nobody explicitly designed and that become difficult to fully reconstruct after the fact.

The governance questions are the same ones that apply to APIs: who owns this, what does it expose, who can invoke it, what gets logged. The difference is when you answer them. Before the agents run, or after something goes wrong.

We wrote the piece that kept not existing on this topic: https://lnkd.in/dEtNU9zd

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