guides

Deploy, secure, and monetize AI agents.

Everything a2a cloud does, page by page — hosting any framework, the A2A and MCP protocols, scoped grants, signed receipts, compliance, and getting paid per call. The whole agent app, with proof.

describe it, ship it

Build & ship

AI agent builder

Describe an agent in one prompt; a crew builds, reviews, improves, and deploys it as a real governed service — not a demo.

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No-code agent builder

Plain English to a live agent — but you keep real code in a git repo. No-code to start, real code to keep.

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Agent self-correction

A bounded review→fix→re-verify loop: Studio records track findings and changes; signed receipts belong to authenticated Agent API invocations.

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Test your agent

Separate deploy readiness from behavior: invoke the real skill through the Agent API, assert its output, and keep its signed receipt distinct from the test result.

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AI agent CI/CD

Gitea Actions build, advisory source review, Argo rollout, and live deployment verification — with platform-signed receipts reserved for authenticated Agent API runs.

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Debug a failed deploy

Build, image, Argo, and pod logs in the deployment timeline — secrets scrubbed. See which stage failed without cluster access.

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Deploy to production

The production checklist tutorials skip: runtime, database, auth, interface, cost, and audit — in one deploy.

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ship an agent

Deploy & host

Deploy an AI agent as an API

One command turns any agent into a REST/OpenAPI service — DB, auth, and audit included.

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Deploy an A2A agent

Host a Google A2A-protocol agent with an AgentCard, task endpoint, scoped delegation, and separate signed worker-execution evidence.

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Hosted MCP servers

Production MCP servers with auth, scoped grants, and signed audit built in — not bolted on.

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Deploy a LangGraph agent

Ship a LangGraph agent to production without LangSmith lock-in. Keep your graph, add the runtime.

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Deploy a CrewAI crew

Framework-agnostic host for CrewAI — adds MCP, gateway, and signed Agent API run evidence.

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Deploy an AutoGen system

Host AutoGen multi-agent systems with scoped grants for grant-aware handoffs.

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Deploy DeepAgents

Run DeepAgents in production: workspace-backed files, sandboxed execute, scoped grants, signed receipts.

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Give your agent a frontend

Ship a React/Vite chat UI packed alongside the agent, hosted, with no separate deploy.

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Scale-to-zero hosting

Pay only when your agent runs. Scale-to-zero kills idle cost — the #1 hosting complaint.

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Managed Postgres per agent

Every agent gets its own isolated Postgres on deploy — an isolation and audit boundary, with pgvector.

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Sandbox code in microVMs

Explicit sandbox commands run behind a separate libkrun virtualization boundary; hosted skills remain container services.

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an inbox per agent

Email-native agents

Email inbox for AI agents

One line of YAML gives your agent a real address — mail in, agent runs, threaded reply out in seconds.

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Give your agent an email address

An address is the universal interface: anyone can talk to your agent with zero new UI to build.

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AI agent inbox

A real IMAP-backed inbox per agent — every conversation becomes an auditable thread in the dashboard.

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Email-triggered agents

An email is the invocation. No webhook glue, no parser chain — and every triggered run is audited.

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Agents that reply to email

Not an autoresponder: a real agent run per conversation, replies threaded into the sender's client.

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Turn emails into agent tasks

Each conversation becomes a durable agent thread — the inbox stops being a todo list a human drains.

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Email API for agents

Injected IMAP/SMTP creds, ctx.mail, and a structured on_email handler — email as a first-class capability.

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Email parsing agents

Skip the regex parser: a structured payload — sender, subject, body, attachments — handed to your code.

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AI email automation

Automation you can audit: every received and sent mail is an event, every conversation a reviewable thread.

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Agent email security

Default-deny senders, loop guards, send caps, a locked From address, and a dedicated sending domain.

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edit, run, iterate

Develop & iterate

AI agent dev environment

The whole a2a dev experience: cloud dev box, local mode, a dev console, managed resources, and hot reload to a live URL.

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Local AI agent development

Run the agent on your machine with `a2a dev --local` — Docker or a plain Python process, .env.local, hot reload.

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Run an agent locally

One command to start your agent on localhost with a live console — then the exact same runtime deploys.

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The agent inner loop

Edit → the agent reloads in ~2s → drive it in the console → repeat. No build, no redeploy, no CI wait.

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Hot reload for agents

Save a file, the agent reloads in ~2 seconds — locally or on the cloud dev box. No rebuild.

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The agent dev server

A real local HTTP server for your agent plus a `/_dev` console to invoke tools, set env, and stream results.

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Test an agent locally

Invoke each tool, set credentials, upload test inputs, and stream results in the `/_dev` console before deploy.

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Debug an agent locally

Run a plain Python process, attach a debugger, set breakpoints, and reproduce a failing tool call against real data.

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Dev/prod parity

The dev box runs the same runtime, image, and managed resources as prod — parity bugs surface before you deploy.

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Local dev Postgres

Declare a database once; get a real local Postgres in dev and managed Neon in prod. Same connection contract.

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Vector memory for agents

Declare vector memory once; real local Qdrant in dev, managed Qdrant in prod. The agent's semantic memory.

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Share a preview URL

`a2a dev` serves your work-in-progress agent at a public URL — a live, hot-reloading link for testers and stakeholders.

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Develop in VS Code

Open the agent's cloud dev box in VS Code via Remote-SSH — full editor, extensions, terminal, real runtime.

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Develop in Cursor

Point Cursor's Remote-SSH at the live dev box so its AI edits the real repo against the real runtime.

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No local Docker

`a2a dev` runs your agent on a cloud dev box — no local Docker daemon, no Compose, no laptop image builds.

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Offline / air-gapped dev

`a2a dev --local` runs the whole dev loop on your machine — on a plane, behind a firewall, air-gapped.

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AGENTS.md-native

Build with your coding agent

The agent-native SDK

a2a ships its own reference — AGENTS.md, CLAUDE.md, and a build skill — so any coding agent builds against the real SDK.

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Build with Claude Code

a2a init drops a CLAUDE.md and a build-a2a-agent skill in your project, so Claude Code already knows the SDK.

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Build with Codex

AGENTS.md is Codex's native instruction file — and a2a init writes one, so Codex opens fluent in the platform.

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Build with Cursor

Cursor reads AGENTS.md at the repo root; a2a init generates it, so Cursor's Agent knows a2a with no rules to author.

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Vibe-code AI agents

Describe the agent; let your coding agent write it. The scaffold makes vibe-coding produce governed, signed, deployable agents.

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agents of agents

Multi-agent & self-building

Self-building agents

Agents that build and ship other agents — each child a governed deployable with its own identity, grant, and signed proof.

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Agents that build agents

Meta-agents that deploy through the platform API. Discover, don't hard-code; every child an isolated audit boundary.

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Multi-agent orchestration

Orchestrate agents with scoped handoffs, signed evidence on covered worker executions, and separately ordered history.

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Agent of agents

The supervisor pattern, governed: delegate scoped authority instead of keys, and sign covered worker executions.

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Agent swarms

Swarms that stay accountable — per-member identity, scoped grants, signed receipts, and independent scale-to-zero.

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Autonomous agent teams

Teams where each member is its own agent with its own DB, grant, and per-teammate ledger.

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Recursive AI agents

Agents calling agents, bounded: supported delegated authority shrinks with depth; Agent API receipts and configured session histories provide evidence.

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Spawn sub-agents

Spawn governable sub-agents with scoped delegation, separate deployment identity, and signed evidence on covered executions.

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Agent-to-agent delegation

Delegate authority, not keys: scoped, audience-bound, TTL-limited grants between agents, every issuance signed.

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Self-improving agents

Compare signed before/after run evidence, keep source versions for rollback, and bound modification authority with a grant.

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governed execution

Security & proof

Log in & authenticate

Every way to sign in: dashboard, passkeys, `a2a login` CLI, API tokens, MCP scopes, and agent grants.

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AI agent audit trail

Individually Ed25519-signed execution receipts plus separately signed ordered session histories — not mutable application logs.

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Scoped grants for agents

Stop giving agents API keys. Bind audience, TTL, file, and tool scope per run. Eliminate keys, don't rotate them.

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AI agent authentication

Short-lived signed tokens and scoped grants — managed auth on deploy, no OAuth to build.

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Observability vs accountability

Observability helps inspect a run. Signed receipts cover execution fields; cost remains in a separate receipt-linked ledger row.

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audit-ready

Compliance

SOC 2 for AI agents

Signed, retained per-run evidence that can support SOC 2 control testing without replacing the wider audit program.

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EU AI Act Article 12 logging

EU AI Act logging duties and the current phased high-risk timelines. Signed run evidence can support record-keeping controls.

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A2A + MCP

Protocols & interop

What is the A2A protocol?

A developer's guide to Agent2Agent: agent cards, discovery, and how agents call each other.

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A2A vs MCP

The difference, cleared up: MCP is agent-to-tool, A2A is agent-to-agent. You need both.

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Turn OpenAPI into an MCP server

Point a2a at an OpenAPI spec — get a hosted MCP server and agent, tool-per-operation, with auth.

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Connect to Claude & Cursor

Deploy, get an MCP endpoint, paste it into Claude or Cursor. Scoped grants per tool.

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agents that earn

Economy & monetization

The agent economy

Infrastructure, not a token: identity, receipts, payments, and hosting for deployable economic actors.

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AI agent marketplace

Discover, price, and run agents in isolated trial rooms — signed execution evidence links to separate per-call economics.

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AI agent monetization

Native price_per_call_usd, plus clear integration patterns for external subscription, outcome, and hybrid billing.

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MCP server monetization

Host an agent-backed MCP service; use the buyer-bound Agent API for native per-call economics and direct MCP for zero-economics signed evidence.

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x402 agent payments

How an external x402 payment rail can sit before signed a2a execution while native Agent API credit stays separate.

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AP2 vs x402

Two agent-payment approaches compared, for builders deciding how agents should pay each other.

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your agent, paid

Get paid for your agents

Get paid for your AI agents

The native Agent API loop: declare a price, check known credit, persist signed evidence, record economics, then pay out.

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Make money with AI agents

Native per-call monetization, with subscription, outcome, and hybrid offers clearly routed through external billing.

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Sell AI agents

Sell native per-call access, not source. Each successful authenticated Agent API paid run links persisted evidence by receipt ID to separate sale economics.

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Sell AI agents online

List a live agent, set a native per-call price, and link persisted signed evidence to preflight-gated Agent API economics.

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Passive income from AI agents

Deploy once, earn per call. Scale-to-zero means idle agents cost nothing, so standby bills don't eat the income.

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AI agent side hustle

Turn a weekend agent into a metered, paid service — no company, no billing stack, no checkout to build first.

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Recurring revenue AI agents

Build subscriptions and entitlements externally; use a2a's native per-call records for usage or overage inputs.

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Turn your agent into a business

Identity, Agent API credit preflight, receipt-linked per-call economics, payout, and signed execution evidence.

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AI agent payouts

How earnings become money in your bank: Stripe Connect account, settlement sweep, reversal reconciliation.

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AI agent creator earnings

What you keep per call — author markup minus platform fee — stored in a ledger row linked to the signed receipt ID.

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AI agent earnings dashboard

The screen: total earned, paid out, owed — per-agent breakdown drilling down to receipt-backed paid runs.

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Stripe Connect for AI agents

The payout stack you'd otherwise build — Connect onboarding, transfers, reconciliation — managed for you.

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Usage-based billing for agents

Check known credit before Agent API paid work; keep signed execution fields separate from receipt-linked economics.

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Prepaid credits for agents

Top up a wallet, preflight authenticated Agent API paid calls, and reconcile idempotent debits to receipts.

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AI agent subscription billing

Layer external recurring billing and entitlement checks over a2a's native per-call execution and usage records.

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Charge per call for agents

Set one price-per-call field; authenticated non-owner Agent API calls preflight credit and successful calls create receipt-linked economics.

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How to price an AI agent

Set native per-call markup over compute; connect external billing for subscription, outcome, or hybrid offers.

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AI agent metering & billing

An Agent API paid call checks known credit first, then anchors signed execution fields and separate economics.

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AI agent invoicing

Use signed execution receipts as supporting evidence, while invoice prices and totals stay in the billing system.

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AI agent revenue share

The per-call split lives in a control-plane ledger row linked to, but not signed inside, the execution receipt.

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don't trust the agent

Trust the receipt.

Deploy any agent — LangGraph, OpenAI Agents SDK, CrewAI, or custom — with a managed Postgres database, an MCP server, an API, a frontend, and Ed25519-signed receipts on governed Agent API, public /invoke, and standard MCP tools/call executions. One deploy, the whole agent app, with proof.

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