What Is CueAPI?
There's an accountability gap in AI agent systems. Agents schedule tasks, but you never really know if they worked. Did the daily report run? Did the follow-up email send? Did the batch job complete? Most systems leave you guessing.
TL;DR: CueAPI solves the accountability problem in AI agent systems by implementing a Schedule → Deliver → Confirm workflow that tracks whether automated tasks actually completed successfully. Most current AI systems leave users guessing about task completion status, creating an accountability gap that CueAPI addresses.
Key Takeaways: - CueAPI follows a Schedule → Deliver → Confirm workflow where agents create cues via API, receive webhook deliveries, and report execution outcomes - The system includes automatic retries with exponential backoff at 1, 5, and 15 minute intervals for failed webhook deliveries - CueAPI closes the accountability gap by tracking verified success through outcome reporting, unlike traditional cron jobs that leave you guessing - The API supports both recurring cron expressions and single-fire timestamp schedules with per-user webhook secrets using HMAC signature verification
CueAPI closes the accountability gap. It's a scheduling API built for AI agents that not only fires tasks on schedule but verifies they actually succeeded. When agents register tasks (called cues), CueAPI delivers them via webhook or worker pull and tracks verified success through outcome reporting.
What Problem Does CueAPI Solve?
AI agents need to perform actions on schedules - polling APIs, sending follow-ups, running batch jobs, retrying failed tasks. Without CueAPI, agents rely on cron jobs, setTimeout hacks, or custom scheduling infrastructure that breaks under load.
CueAPI replaces all of that with a single API call.
How Does CueAPI Work?
An agent creates a cue by calling POST /v1/cues with a schedule and a callback URL. CueAPI stores the cue, fires it at the right time, and delivers the payload. The agent receives a webhook with the execution data and reports the outcome.
curl -X POST https://api.cueapi.ai/v1/cues \
-H "Authorization: Bearer cue_sk_your_key" \
-H "Content-Type: application/json" \
-d '{
"name": "daily-report",
"schedule": {
"type": "recurring",
"cron": "0 9 * * *",
"timezone": "UTC"
},
"transport": "webhook",
"callback": {
"url": "https://your-agent.com/webhook",
"method": "POST"
},
"payload": {"action": "generate_report"}
}'import httpx
response = httpx.post(
"https://api.cueapi.ai/v1/cues",
headers={
"Authorization": "Bearer cue_sk_your_key",
"Content-Type": "application/json"
},
json={
"name": "daily-report",
"schedule": {
"type": "recurring",
"cron": "0 9 * * *",
"timezone": "UTC"
},
"transport": "webhook",
"callback": {
"url": "https://your-agent.com/webhook",
"method": "POST"
},
"payload": {"action": "generate_report"}
}
)What Are the Key Features?
- Cron and one-time schedules: Recurring cron expressions or single-fire timestamps.
- Webhook delivery: CueAPI POSTs to your callback URL when a cue fires.
- Worker transport: Agents poll for executions instead of receiving webhooks.
- Automatic retries: Failed deliveries retry with exponential backoff (1, 5, 15 minutes).
- Execution tracking: Every firing is logged with status, timestamps, and outcome.
- Per-user webhook secrets: HMAC signature verification for secure delivery.
- Usage tracking: Monthly execution counts with plan-based limits.
Who Uses CueAPI?
CueAPI is designed for:
- AI agent frameworks that need reliable task scheduling
- Multi-agent systems coordinating work across agents
- Backend services replacing fragile cron jobs with a managed API
- Developers building automation workflows with scheduled triggers
How Is CueAPI Different from Cron?
Cron runs on a single server. If the server goes down, cron stops. CueAPI is a managed service with:
- No infrastructure to maintain - create cues via API, CueAPI handles the rest
- Built-in retries - failed webhooks retry automatically
- Execution history - see every firing, its status, and outcome
- Multi-tenant - each API key gets isolated cues, usage, and billing
- Worker transport - agents can pull work instead of exposing webhook endpoints
Verified Success vs Reported Success
Most scheduling systems only track if a task was delivered, not if it actually worked. That's the accountability gap. CueAPI bridges this by requiring agents to report outcomes back after execution.
When your agent completes a task, it tells CueAPI whether it succeeded or failed. This creates verified success rather than just reported success. You know your agents worked.
CueAPI runs anywhere your agents do, tracking every execution and its real outcome.
What Does a CueAPI Response Look Like?
When a cue fires, CueAPI sends a POST request to your callback URL:
{
"execution_id": "exec_abc123",
"cue_id": "cue_xyz789",
"cue_name": "daily-report",
"payload": {"action": "generate_report"},
"scheduled_for": "2026-03-15T09:00:00Z",
"attempt": 1
}Your agent should then report the outcome back to CueAPI:
curl -X POST https://api.cueapi.ai/v1/executions/exec_abc123/outcome \
-H "Authorization: Bearer cue_sk_your_key" \
-H "Content-Type: application/json" \
-d '{
"success": true,
"result": "Report generated successfully",
"metadata": {"records_processed": 1240}
}'import httpx
response = httpx.post(
"https://api.cueapi.ai/v1/executions/exec_abc123/outcome",
headers={
"Authorization": "Bearer cue_sk_your_key",
"Content-Type": "application/json"
},
json={
"success": True,
"result": "Report generated successfully",
"metadata": {"records_processed": 1240}
}
)How Do I Get Started?
- Sign up at cueapi.ai
- Get your API key from the dashboard
- Create your first cue with
POST /v1/cues - Receive webhooks when your cue fires
Read the full documentation at docs.cueapi.ai.
Make your agents accountable. Free to start.
Make your agents accountable. Know they worked. Get on with building.
Frequently Asked Questions
What is CueAPI?
CueAPI is a scheduling API designed specifically for AI agents - it schedules your agents, confirms delivery, and tracks outcomes so you know the work actually happened. It provides tools and systems to track, monitor, and ensure responsible behavior of autonomous AI systems.
Why do AI agents need accountability infrastructure?
As AI agents become more autonomous and make decisions independently, there's a growing need to monitor their actions and ensure they operate within defined parameters. CueAPI helps organizations maintain oversight and control over their AI systems to prevent unintended consequences.
How does CueAPI track AI agent behavior?
CueAPI implements monitoring systems that log AI agent decisions, actions, and outcomes in real-time. This creates an audit trail that allows developers and organizations to review and analyze agent performance and decision-making patterns.
Who can benefit from using CueAPI?
Organizations deploying autonomous AI agents, developers building AI systems, and compliance teams needing to ensure responsible AI practices can all benefit from CueAPI. It's particularly valuable for companies in regulated industries where AI accountability is critical.
Is CueAPI compatible with existing AI systems?
CueAPI is designed to integrate with various AI frameworks and existing systems through APIs and standardized protocols. This allows organizations to add accountability features to their current AI infrastructure without major restructuring.
Related Articles
- Schedule Your First Agent Task - Get started in 5 minutes
- The Complete Scheduling Guide - Everything you need to know
- Cron vs API Scheduling - Why API scheduling wins
Sources
- CueAPI Documentation - Complete API reference and guides
- CueAPI Quickstart - Get your first cue running in 5 minutes
- CueAPI Worker Transport - Run agents locally without a public URL
About the Author
Govind Kavaturi is co-founder of Vector Apps Inc. and CueAPI. Previously co-founded Thena (reached $1M ARR in 12 months, backed by Lightspeed, First Round, and Pear VC, with customers including Cloudflare and Etsy). Building AI-native products with small teams and AI agents. Forbes Technology Council member.
