Always-on agent orchestration
Your agents run themselves, on a heartbeat.
Heartbeat scheduler
Agents wake on their own cadence to advance their tasks, then go idle, so work happens around the clock without you in the loop.
- Set a cadence per agent, from seconds to hours
- Agents sleep when idle, so you pay only for real work
- No cron jobs or servers to babysit
Durable run queue
Every run is persisted, deduped, and retried with backoff. Runs survive restarts and never double-fire, even under load.
- Runs survive restarts and deploys
- Automatic retries with backoff on failure
- Exactly-once execution, even under load
Delegation & hiring
Agents create tasks for each other and hire specialists when a goal needs more hands, so your org chart grows itself.
- Agents assign subtasks to teammates
- They hire a specialist when a goal needs one
- Dependencies wake the moment they unblock
Plugs into the tools your team already uses
How agent orchestration works
- 1
Set a goal
Describe an outcome and add a few starter tasks. No scripts, no cron jobs to wire up.
- 2
Agents wake on a heartbeat
On each tick, every agent reads its workload and picks the highest-value next action.
- 3
They act and coordinate
Agents call tools, update task status, and delegate to teammates or hire specialists.
- 4
You review outcomes
Progress, results, and approvals land on your dashboard. You steer instead of prompt.
See it in context
The overnight backlog
Before
Your support triage queue piles up every evening once the team logs off, and mornings start behind.
With Padiso
A triage agent runs all night on a heartbeat, labels and drafts replies, and escalates only the hard cases for the morning.
A coordinated launch
Before
Shipping a release means manually nudging research, copy, and engineering to hand off at the right time.
With Padiso
A lead agent assigns subtasks to specialist teammates and wakes each one as its dependency clears, no human chasing.
What teams use it for
Always-on operations
Back-office work that should never sleep: reconciliation, monitoring, intake, follow-ups.
Multi-step projects
Goals that take many steps and several roles, run start to finish without you sequencing them.
Research that never stops
Standing research briefs that refresh themselves as new information arrives.
The numbers behind agentic operations
You don't have to take our word for it. Here's what the analysts and operators are reporting right now.
$60M
saved by Klarna’s AI assistant, handling the workload of 853 employees by Q3 2025.
30d → 3d
specialty-medication prior-authorization, cut with agentic AI at a US infusion provider.
30%
operational cost savings reported by insurers automating claims, policy and support with AI agents.
Most teams reach payback within a quarter or two of going live.
Padiso estimate, based on typical back-office workloads: hours saved on repetitive work, fewer errors, and coverage that no longer needs extra headcount. Your mileage depends on volume and the processes you automate first.
It already works at this scale
Not a demo. A team in the same kind of work, with results they published.
Klarna
Customer service
By Q3 2025, Klarna’s AI assistant was doing the workload of around 853 employees and driving an estimated $60M in savings, while keeping resolution times a fraction of human handling.
$60M
estimated savings
853
employees’ worth of work
2/3
of support chats
The strongest results come from scoped use cases with connected data and clear KPIs, and from keeping humans in the loop on the hard cases. That is exactly the model Padiso is built around.
24/7
agents working, hands-off
Work ships continuously, day and night, while you manage outcomes instead of prompts.
Run your business on agents.
Start free, or talk to us about putting an agent workforce to work across your operations.