Answers, straight.
For both the engineers reading this in their IDE and the heads-of-function reading it in a browser.
The basics.
What is AgentBundle?
A platform for defining AI agents once and shipping them to every AI tool your team uses — Claude Code, Cursor, GitHub Copilot, OpenCode, Gemini, Codex, Windsurf. You author the agent in one place; it shows up consistently in every runtime, with the same prompts, the same connections, and the same safety rules.
What’s an “AI agent” — in one sentence?
A reusable prompt with its context attached — the docs it can read, the external services it can talk to, the rules it has to follow. Think of it as a saved-and-shareable chatbot specialized for a specific job.
Who is this for?
Heads of function who own a canonical playbook today — a brand voice doc, a qualification rubric, a handbook, an escalation tree. Plus every team that consumes those playbooks. Engineering teams scaling AI tooling. Anyone whose organization has more than one person using AI.
Do I need to know how to code?
No. The dashboard wizard lets you build agents by pasting in your docs, picking which external services they connect to, and writing the prompt as plain text. Engineers can do everything via APM CLI if they prefer; non-engineers do everything in the browser.
When AgentBundle is worth it.
I already use Claude Code or Cursor. Why do I need this?
Those tools execute agents at runtime. They don’t help your team author, version, govern, or share them. If you’re one person doing AI well in one tool, you don’t need AgentBundle. If you’re ten people across five tools all maintaining slightly different prompts, you do.
Our playbooks live in Notion already. Isn’t that enough?
Docs answer questions when someone reads them. Agents answer questions when someone asks them — inside the AI tool they’re already in. The marketing lead writes the brand voice doc; engineers, sales, and support all run the brand voice agent. Same source of truth; this version actually gets used.
How is this different from LangChain / CrewAI / AutoGen?
Those are agent runtimes — code frameworks an engineer uses to build a custom agent. AgentBundle is one step upstream: a place to author an agent without code and ship it as an open APM package that any runtime can install. They don’t compete; you can use both.
Why would non-engineers care?
Most of your organization’s institutional knowledge lives with non-engineers. The marketing lead owns the brand voice; the customer-success lead owns the triage rubric; the HR partner owns the handbook. They author docs today and watch them rot. AgentBundle lets them author an agent instead — same content, but a format engineers and the rest of the org can consume in any AI tool. No technical background required.
When is AgentBundle NOT the right fit?
Solo developers running one agent in one IDE — you don’t need a platform for that. Teams that need to build deeply custom multi-agent orchestration today — use LangChain, CrewAI, or AutoGen and run them in your own infra. AgentBundle is for organizational reuse, not solo prototyping and not arbitrary code.
The mechanics, briefly.
What does “publishing” an agent actually do?
You build the agent in the dashboard (or CLI). When you hit Publish, it’s versioned with a semver release, scanned for secrets and prompt-injection patterns, optionally routed through your reviewers, and packaged as an APM bundle. The package is installable in any of the 7 supported runtimes.
What’s APM?
APM (Agent Package Manager) is Microsoft’s open packaging spec for AI agents — like npm for JavaScript or Docker images for containers. AgentBundle outputs APM-compatible packages; they’re portable to any APM-aware runtime, not locked to us.
What’s an MCP server?
MCP (Model Context Protocol) is the open standard for connecting AI agents to external systems — your GitHub repo, your Linear issues, your Salesforce CRM, your Zendesk tickets. You register the MCP server once at the org level; your agents reference it. The MCP server runs in your infrastructure (or the vendor’s), not on AgentBundle.
Where does the AI inference actually happen?
In whichever runtime you install the agent into. Claude Code runs on Anthropic’s API; Cursor on whichever model you pick; Copilot on GitHub’s models. AgentBundle stores the definition. We don’t run the inference and we don’t see the runtime conversations.
What if I want to leave AgentBundle later?
Export your agents anytime — they’re standard APM packages, runnable on any APM-compatible runtime, independent of us. No vendor lock-in by design. The whole point of using an open package format is that the format outlives any one platform.
Can my team try this without committing?
Yes. The Free tier is permanent (no card needed) — 5 members, 5 agents, every security scan, every supported runtime. Upgrade only when your team grows or you need governance features. See /pricing.
The honest answers.
Do you train AI on my data?
No. We don’t operate an AI model — there’s nothing to train. Your agent definitions, your runtime conversations, and your communications with us are never used to train any model, ours or any sub-processor’s. See /legal/privacy.
Is my data secure?
Every agent runs through a secret-scanner and a prompt-injection scanner on every publish (Free tier and up — not gated). Role-based access controls who can author, edit, and approve. Versioning plus an audit log captures every change. See /security for the full posture.
Where is my data stored?
United States, on managed PostgreSQL operated by Neon. Encrypted at rest by the database; TLS-protected in transit. The full sub-processor list is in /legal/privacy. International transfers are handled by a Data Processing Addendum on request.
Who in my organization can see my agents?
By default, every member of your organization. The author can scope an agent to specific teams, or keep it private to themselves and chosen collaborators. Visibility is set per agent, not per department.
What if a published agent has a bug — or worse?
Three escape hatches. Revert rolls the live version pointer back to a prior version. Deprecate marks a version with a warning so consumers know to migrate. Recall blocks new installs entirely (HTTP 410 Gone) — for serious bugs, leaked secrets, or policy violations. All audited. See /security.
Is there an SLA?
No formal SLA on the standard plans. Custom written SLAs are available on enterprise contracts. The platform is hosted on Vercel + Neon; both have their own published uptime tracks if you want a baseline.
Pricing, plans, money.
What happens when I hit my plan limit?
You’ll see a clear error in-product when you try to add a new member or agent past your tier’s cap. Existing items keep working — nothing gets cut off retroactively. Upgrade your plan or remove some, and you’re back to creating.
Can I switch plans?
Yes, anytime. Upgrades take effect immediately and prorate. For downgrades, email hello@agentbundle.dev and we’ll handle the timing with you.
Do you offer refunds?
Monthly subscriptions are non-refundable. Annual subscriptions are prorated and refundable for the unused portion within 14 days of the most recent renewal. Details in /legal/terms §8.3.
How does annual billing save ~14%?
Annual = roughly two months free vs. paying monthly. Toggle on the /pricing page to see the side-by-side numbers.
Question we didn’t answer?
Email hello@agentbundle.dev — replies usually within one business day.