Research & analysis
News and signal monitoring
Watch sources continuously and alert on what matters.
- Workflows
- AI
- Utilities
- Streams
The scenario
Agents watch a set of sources and notify a team when something relevant to their topic appears.
How it works
- 1Scheduled workflows fetch new items.
- 2A cheap model classifies relevance.
- 3Relevant items are published as events for other agents to act on.
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
01Scheduled workflows fetch new items.
queued- Ahura Workflowsworkflow.runqueued
status: queued · 0 / 36 steps completed
- Ahura AIai.classifyqueued
status: queued · 0 / 150 model calls
02A cheap model classifies relevance.
queued- Ahura Utilitiesutilities.clean_article_extractorqueued
status: queued · 0 / 120 documents processed
03Relevant items are published as events for other agents to act on.
queued- Ahura Streamsstreams.tailqueued
status: queued · 0 / 300 events routed
Services in this partition
Workflows
0 calls · 0.00 ACU
AI
0 calls · 0.00 ACU
Utilities
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
Monitoring tools either alert on everything or miss what matters, and they run all day whether or not anything happens. On Ahura OS scheduled workflows fetch, a low-cost model decides what is relevant, and only relevant items become events other agents act on. You pay per check and per classification, so a quiet day is nearly free. Stored items carry an expiry, which keeps a monitoring feed from quietly becoming a permanent archive of everything you ever saw.
What each capability replaces
- Ahura WorkflowsInstead of writing retry, timeout and approval plumbing, you declare the steps and the platform guarantees the behaviour and the history.
- Ahura AIInstead of a model gateway and several vendor contracts, one priced call reaches several models and chooses a cheap one when it can.
- Ahura UtilitiesInstead of a function platform to host, common helpers are ready to call and billed per invocation.
- 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
Always-on servers polling feeds, plus keyword alerts that flood inboxes with noise.
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