Building an AI agent is easy. Running it responsibly at scale is the hard part.

Jalebi is the platform that sits between “we built an agent” and “we trust it with production traffic, real cost controls, and our compliance requirements.” No AI infrastructure team required.

Built on Amazon Bedrock AgentCore.

Every AI agent tool skips the same question

What happens when it goes from 10 users to 10,000 — who's paying for it, and does your infrastructure survive without a rebuild or a runaway bill?

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Cost governance

A six-level quota chain — org, workspace, user, API key, customer, agent — caps spend and attributes cost automatically. Give finance a budget and marketing a separate one; both get warned before they hit it. Most agent-building tools have no concept of this.

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Scales without a rewrite

Every conversation runs in its own isolated instance on serverless infrastructure. When one account's limits become the ceiling, workspaces already span multiple accounts — a real design iteration we went through ourselves before any customer could hit that wall.

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Your data stays yours

Deploy entirely inside your own cloud account. Zero data leaves your network. Same console, same features, same guardrails — just running where your compliance team can see it.

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Accountable actions

When an agent writes to your CRM or sends an email, the system on the other end should know whether a person did it or an agent did it for them — and which person. Most agent tools send one service account and the trail ends there. We're building the delegation chain in, so your audit log still names a human.

One agent. Stream it or don't.

Which one you want depends on who is waiting. A backend job needs a single value it can store and act on. A person needs to ask, see the work, and follow up without starting over. Same agent either way, and nothing is redeployed to switch.

Just the answer

For server-side work. One value to store, log, or act on.

Request
curl -X POST \
  https://api.jalebi.chat/agents/agt_xyz/invoke \
  -H "Authorization: Bearer jc_sk_..." \
  -H "X-Jalebi-User-Id: user_42" \
  -H 'X-Jalebi-Tags: {"dept":"support"}' \
  -d '{
    "message": "Where is my order?",
    "sessionId": "ses_abc",
    "context": { "account_tier": "premium" }
  }'
Response
{
  "success": true,
  "data": {
    "response": "Your order shipped Tuesday.",
    "sessionId": "ses_abc",
    "metadata": {
      "model": "mistral.ministral-3-14b-instruct",
      "latency": 1430
    }
  }
}

The least to integrate: nothing stays open and there is no client state to manage. Fits webhook handlers, batch jobs, and anywhere a server is the one asking. The agent still called its tools and still kept the session.

A conversation

For people. Ask, see the work, ask again, on one connection.

AG-UI over WebSocketlive transcript
# 1  authorize→ POST /agents/agt_xyz/connect?protocol=agui← { "endpoint": "https://bedrock-agentcore.us-west-2...",      "paths": { "ws": "/ws", "sse": "/invocations" },      "token": "eyJhbGci...", "expiresIn": 900 }# 2  open one socket and keep it→ GET <endpoint>/ws   Upgrade: websocket   Bearer eyJ...← {"type":"connected","sessionId":"ses_abc",   "clientsInSession":1}# 3  ask→ {"threadId":"ses_abc","messages":[     {"role":"user","content":"Where is my order?"}]}← {"type":"RUN_STARTED","threadId":"ses_abc"}← {"type":"TOOL_CALL_START","toolName":"get_order"}← {"type":"TOOL_CALL_RESULT","result":{"eta":"Tue"}}← {"type":"TEXT_MESSAGE_CONTENT","delta":"Your order "}← {"type":"TEXT_MESSAGE_CONTENT","delta":"shipped Tuesday."}← {"type":"RUN_FINISHED","threadId":"ses_abc"}# 4  follow up on the same socket→ {"threadId":"ses_abc","messages":[     {"role":"user","content":"Send it to my office"}]}← {"type":"RUN_STARTED","threadId":"ses_abc"}← {"type":"TOOL_CALL_START","toolName":"update_address"}← {"type":"TEXT_MESSAGE_CONTENT","delta":"Done - at work"}← {"type":"RUN_FINISHED","threadId":"ses_abc"}

The connection stays open, so turn two skips the handshake and the agent still holds the thread — nobody has to repeat themselves. Every client in that session gets the same frames too, so one run feeds the person asking, a supervisor, and a dashboard, billed once rather than three times. Only need one answer streamed? Post to sse instead.

Know who did what, and what it cost

Three things ride along with every call, whichever way you take the reply — a header when you post a message, a property on the stream when you open one. Which means your finance reporting does not depend on which one your engineers picked.

Who spent it

Every run is attributed to the person behind it, so you can set per-user limits and put a name against the cost.

Whose budget it lands on

Tag a run by department, project, or customer and the spend groups the way you already report it.

What the agent knew

Pass the facts for this caller — their plan, their entitlements, their locale — at call time, never baked into a prompt.

The same agent is reachable over MCP for IDEs like Claude Desktop and Cursor, and over A2A for other agents. One deployment, no adapters needed.

Not another AI framework. A management layer on proven infrastructure.

Every agent Jalebi deploys is a real Amazon Bedrock AgentCore resource in your own AWS environment. We didn't build a new runtime to bet your business on — we built the console, the governance, and the guardrails that AgentCore itself doesn't provide out of the box.

Think Vercel or Shopify to AWS's raw infrastructure: same underlying platform, but with the operational layer a business actually needs to run it — without hiring a team to build that layer first.

See the full platform →
🏗️
Infrastructure
AWS Bedrock AgentCore
→
🍛
Platform
Jalebi runs on top

We put our own products on Jalebi before yours

Jalebi is early, and we would rather be precise about that than impressive about it. The platform is built — the quota chain, multi-account workspaces, and BYOC deployment above are implemented and covered by several thousand tests. What they are not yet is battle-tested by production traffic. Our own first app, live cricket scoring for a club that is ready to use it, is the next thing we put on the platform, specifically so we are not asking you to be our first production customer. If you want a platform whose team will tell you plainly what is proven and what is merely written, we'd like to talk.

Your agents. Your data. Your rules. Zero infrastructure to manage.

jalebi.chat — AI agent platform for teams that need to trust what they ship