Research & analysis
Market research swarm
Many agents research competitors in parallel and merge their findings.
- Memory
- Store
- Workflows
- Streams
The scenario
A team needs a landscape report on twenty competitors by Friday. One agent per competitor gathers facts while a coordinator assembles the result.
How it works
- 1Each agent writes notes to shared memory as it goes.
- 2Sources and raw pages are saved to storage.
- 3A workflow merges everything into one report when all agents finish.
An illustrative scenario, not a customer story.
Interactive simulation
Simulate this case study
Watch the partition come up, each step run, and usage build as it goes. Simulated: volumes, timings and prices are illustrative.
Partition
—
Status
not created
Elapsed
00:00
- Provisioning
- Processing
- Complete
01Each agent writes notes to shared memory as it goes.
queued- Ahura Memorymemory.setqueued
status: queued · 0 / 240 keys written
- Ahura Storestore.getqueued
status: queued · 0 / 48 objects stored
02Sources and raw pages are saved to storage.
queued- Ahura Workflowsworkflow.definequeued
status: queued · 0 / 36 steps completed
03A workflow merges everything into one report when all agents finish.
queued- Ahura Streamsstreams.tailqueued
status: queued · 0 / 300 events routed
Services in this partition
Memory
0 calls · 0.00 ACU
Store
0 calls · 0.00 ACU
Workflows
0 calls · 0.00 ACU
Streams
0 calls · 0.00 ACU
Spend by service
Event log
- Waiting to start.
Why Ahura OS
Why this is better on Ahura OS
Parallel research fails at the seams: agents duplicate work, overwrite each other’s notes and nobody trusts the merge. On Ahura OS every agent reads and writes the same memory, saves sources to the same store, and a workflow owns the merge, so the final report is assembled by a process with retries and a history, not by whoever finished last. There is no database to design and no queue to run. The whole project is one partition: one bill, one retention window, one thing to freeze or delete when the report ships.
What each capability replaces
- Ahura MemoryInstead of provisioning and securing a cache or database, agents get fast key/value state with expiry already scoped to their partition.
- Ahura StoreInstead of managing buckets, permissions and lifecycle rules, objects follow the partition: they freeze, thaw and expire with it.
- Ahura WorkflowsInstead of writing retry, timeout and approval plumbing, you declare the steps and the platform guarantees the behaviour and the history.
- Ahura StreamsInstead of a message broker to run, agents, workflows and your own systems react to events through managed routing.
And on every use case
One endpoint, not a stack
Memory, storage, compute, inference, identity and messaging share one connection, one identity and one bill. The integration work you would normally own disappears.
Isolation by construction
Every agent’s world is a partition. Separation is how the platform is built, not a convention teams must remember to follow.
Cost follows use
Pay per call and per millisecond. Idle agents freeze to archive pricing. There is no capacity to reserve and nothing running unseen.
Retention and deletion built in
Lifetimes, expiry, freezing and destruction are platform features, so your data policy is enforced rather than aspirational.
Safe autonomy
Spending limits, protected signing keys, approval steps and signed actions let agents work unattended without being unaccountable.
Ready for audit
Designed against SOC 2 and ISO 27001 controls, with every call metered, recorded and attributable.
Compared
The old way of doing things
Analysts or a hand-built script farm: shared spreadsheets, a database someone has to host, and scrapers that break. Notes end up scattered across laptops and drives.
Data retention is built in
Everything this scenario stores lives in one isolated partition with a lifetime you set. Expired data deletes itself, a paused project freezes to low-cost archive, and a finished one can be destroyed completely. Security and compliance