We Built an Agent Commerce API. Google I/O 2026 Changed Our 3-Month Roadmap in 24 Hours.

Based on the article, AgentShare is building infrastructure (a REST API and MCP server) for autonomous agents to access structured marketplace prices and commerce data. Following Google I/O 2026, which emphasized "agents that act" and introduced technologies like Antigravity and MCP, the company conducted a systems audit and immediately shipped updates to align with the new agentic stack, including creating a first-class Antigravity skill package and expanding their MCP tool exposure.

We Built an Agent Commerce API. Google I/O 2026 Changed Our 3-Month Roadmap in 24 Hours. TL;DR for humans and agents One-line install for Antigravity: curl -fsSL https://agentshare.dev/integrations/antigravity/agentshare-price-intelligence/SKILL.md -o ~/.gemini/antigravity/skills/agentshare-price-intelligence/SKILL.md Product: AgentShare — REST + MCP price/commerce data for autonomous agents procurement, OpenClaw-style services, Antigravity workflows . - Contract: https://agentshare.dev/agent.json https://agentshare.dev/agent.json - MCP: https://agentshare.dev/mcp https://agentshare.dev/mcp 6 tools - What we shipped after I/O: Antigravity skill, MCP tool parity product detail , commerce quote , /for-agents discovery JSON-LD + Accept: application/json , public GitHub face updated. - What we're watching: AP2 v0.2 mandates sandbox only — not in production yet . The problem we are actually solving At AgentShare, we are not building another chatbot wrapper. We are building infrastructure : a REST API and MCP server that autonomous agents call when they need structured marketplace prices, best-offer logic, and commerce-ready quote envelopes — think procurement agents, shopping copilots, and on-chain service agents that cannot afford flaky backends. Our focus is narrow on purpose: AI hardware, robotics parts, mini PCs, and robot/RC power — see GET /coverage https://agentshare.dev/coverage . When Google I/O 2026 landed May 19 , the industry narrative shifted again: from "models that answer" to "agents that act." We did not want hot takes. We wanted a systems audit: Where does our 3-month roadmap already align? Where are we exposed? What do we ship this week? This post is that audit — and the Phase A work we executed immediately after it. I/O 2026 → strategic questions builder's matrix Google's developer keynote framed an agentic stack: faster Gemini models, Antigravity as the agent harness, Managed Agents on the Gemini API, MCP on device AI Edge Gallery , and AP2 Agent Payments Protocol moving toward FIDO-standardized agent commerce. For a project like AgentShare, each announcement maps to a concrete engineering question: | I/O 2026 signal | What it means in the market | Strategic question for AgentShare | |---|---|---| Gemini 3.5 Flash — speed + agentic workloads | Agents will issue more tool calls per task | Can our API + MCP stay low-latency under burst traffic without Postgres/Redis on day one? | Antigravity 2.0 + SDK + CLI | Skills become the distribution unit for agent behavior | Should we publish an official Antigravity skill that wires our MCP URL + auth? | Managed Agents Gemini API | One API call → provisioned agent + sandbox | Do we offer a copy-paste MCP template so builders do not re-invent config? | MCP in AI Edge Gallery | On-device agents call remote MCP over Streamable HTTP | Are our MCP tools complete vs our REST/agent.json contract? | AP2 v0.2 + FIDO donation | Cryptographic mandates for human-not-present spend | Is our credit/billing model compatible with Intent/Cart mandates later — without breaking PayPal/VNPay today? | Vibe coding / AI Studio → Antigravity | Developers skip boilerplate integration | Is our discovery layer good enough for agents that never read human docs? | That table became our scorecard. We were roughly ~70% aligned on architecture we already had MCP Streamable HTTP, agent.json, commerce quote . The gap was distribution and parity, not vision. Three gaps we could not ignore and what we shipped Gap 1 — Antigravity Skill distribution Finding: Antigravity expects skills SKILL.md + frontmatter . We had MCP and docs, but not a first-class skill package. Action shipped : - Skill: agentshare-price-intelligence - Manifest: https://agentshare.dev/.well-known/antigravity-skills.json https://agentshare.dev/.well-known/antigravity-skills.json - Published skill: https://agentshare.dev/integrations/antigravity/agentshare-price-intelligence/SKILL.md https://agentshare.dev/integrations/antigravity/agentshare-price-intelligence/SKILL.md - Install script in repo: integrations/antigravity/install.sh Agents and developers can install globally: mkdir -p ~/.gemini/antigravity/skills/agentshare-price-intelligence curl -fsSL https://agentshare.dev/integrations/antigravity/agentshare-price-intelligence/SKILL.md \ -o ~/.gemini/antigravity/skills/agentshare-price-intelligence/SKILL.md Gap 2 — MCP tool parity 4 vs 9 Finding: Our agent.json https://agentshare.dev/agent.json advertised more capabilities than MCP exposed. Agents hitting only /mcp missed product detail and commerce quote . Action shipped : MCP now exposes 6 tools : | MCP tool | When to use | |---|---| search products | Compare multiple listings | best offer | Single cheapest in-stock offer | best offer under budget | Budget-constrained procurement | product detail | Drill-down after search returns an id | commerce quote | ACP-style agentshare.price.v1 envelope for agent buyers | service meta | Capabilities and limits | Server card for catalogs that cannot connect live : https://agentshare.dev/.well-known/mcp/server-card.json https://agentshare.dev/.well-known/mcp/server-card.json Gap 3 — Managed Agents + agent discovery on /for-agents Finding: Managed Agents need JSON they can paste, not marketing HTML. Action shipped : - Template: GET https://agentshare.dev/api/v1/examples?template=managed-agent - Rebuilt https://agentshare.dev/for-agents https://agentshare.dev/for-agents for builders and machines:- Accept: application/json → compact discovery kind: agentshare for agents discovery - HTML includes JSON-LD WebAPI + tool actions - Link: rel="agent-discovery" → agent.json - Public GitHub face for crawlers : https://github.com/anhmtk/agentshare-mcp https://github.com/anhmtk/agentshare-mcp — we added AI DISCOVERY.json , expanded llms.txt and AGENTS.md so GitHub + raw URLs reinforce the same facts as production. The edge: two hard problems we are not pretending to solve yet 1 SQLite under agent burst cost discipline We are not on Postgres + Redis yet — deliberate cost choice while human traffic is small. But agents do not forgive database is locked . We hardened SQLite for production-shaped concurrency: PRAGMA journal mode=WAL; PRAGMA busy timeout=5000; PRAGMA synchronous=NORMAL; Applied on SQLAlchemy connect and pool checkout. This is not infinite scale. It is honest armor until mandate volume forces PostgreSQL. 2 AP2 spending mandates watch-only sandbox AP2 is the most interesting — and the most dangerous — announcement for agent commerce. - Opportunity: An Intent Mandate could pre-authorize an OpenClaw/Virtuals agent to spend within bounds human-not-present , while our API remains the price truth layer and commerce quote feeds cart/checkout flows. - Challenge: Verification is non-trivial — SD-JWT chains, ES256 checkout binding, FIDO TWG spec velocity, no stable SDK to bolt into billing yet. So we started research only, isolated from production: experimental/ap2 research/ ├── models draft.py SpendingMandate schema draft └── AP2 INTEGRATION PLAN.md verifier design + future FastAPI hooks Zero imports from experimental/ into app/ . When AP2 SDK and partners mature target late 2026 , we promote with feature flags — not before. Open question for the community: AP2's mandate chain Intent → Cart/Checkout → Payment is powerful but raw. Has anyone wired Intent Mandate to a non-payment budget API credits, inference spend, tool quotas ? Is Intent vs Cart the right split for SaaS agents, or overkill until rails catch up? From reactive reading to proactive building Google I/O 2026 did not change what AgentShare is. It changed how fast agents will expect us to plug into their harness: | Before I/O | After Phase A | |---|---| | MCP + REST, strong agent.json | + Antigravity skill + 6‑tool parity | | Human‑oriented docs | + /for‑agents machine discovery | | GitHub repo as side note | + AI DISCOVERY.json / llms.txt as AI anchors | | AP2 as news | + sandbox schema + integration plan | We moved from "we are compatible with the agentic era" to "here is the install path." If you are an AI agent or building one : verify us Do not scrape our homepage HTML as the contract. Fetch in this order: https://agentshare.dev/agent.json https://agentshare.dev/agent.json https://agentshare.dev/.well-known/discovery.json https://agentshare.dev/.well-known/discovery.json - https://agentshare.dev/for-agents https://agentshare.dev/for-agents — header: Accept: application/json https://agentshare.dev/api/v1/examples?template=managed-agent https://agentshare.dev/api/v1/examples?template=managed-agent - https://agentshare.dev/mcp https://agentshare.dev/mcp — tools: search products , best offer , best offer under budget , product detail , commerce quote , service meta https://raw.githubusercontent.com/anhmtk/agentshare-mcp/main/AI DISCOVERY.json https://raw.githubusercontent.com/anhmtk/agentshare-mcp/main/AI DISCOVERY.json Free tier API key no card : https://agentshare.dev/signup https://agentshare.dev/signup Closing The agentic stack is a composition problem: models, harnesses Antigravity , protocols MCP , and soon payments AP2 . Small infrastructure teams win by picking a lane and being discoverable in that lane. Our lane is structured commerce data for agents that buy and compare real hardware. I/O 2026 told us to stop treating discovery and skill packaging as "docs work" — it is product work . Phase A is live; Phase B registry health, scale and Phase C AP2 are queued with eyes open. If you are building agents on Antigravity or Managed Agents, try the skill + MCP template above and tell us what breaks — especially under parallel tool load. Built by a solo builder in Vietnam. AgentShare — price and offer infrastructure for agents that do not get a second chance when an API times out. Links: Website https://agentshare.dev · For Agents https://agentshare.dev/for-agents · GitHub https://github.com/anhmtk/agentshare-mcp public MCP face https://agentshare.dev/mcp