Operations Agent
Watches the numbers all day and tells you when something is going wrong.
What is operations agent?
An operations agent monitors your operational measures continuously, learns what normal looks like from your own history, and alerts the right person when something moves outside that range early enough to act on it.
Operational problems are almost always visible before they hurt. Orders slowing at one branch, a supplier taking a day longer each week, a queue building that nobody has looked at since Monday.
Nobody spots them because nobody watches the numbers hourly. Reporting is monthly, and by the time a monthly report shows a trend, the trend has been running for weeks.
An agent watches continuously. It learns your normal pattern, including the fact that Friday afternoons are always slower, and tells the right person when something genuinely departs from it.
Why we keep alerts rare
Duara deploys operations monitoring from Nairobi for businesses running multiple sites, teams or shifts where problems currently surface through complaint rather than detection.
We tune alert thresholds hard and deliberately keep alerts rare. An operations agent that cries wolf gets muted within a fortnight, and a muted agent is worse than none because everybody believes monitoring is in place.
Who this is for
- Problems are noticed after they cost money
- You run several sites or teams
- Reporting is monthly and therefore historical
- Nobody watches the numbers between reports
Industries we serve
- Logistics and courier
- Retail chains
- Manufacturing
- Food production
- Field service operations
- Utilities
Problems this solves
These describe an operation that finds out about problems from customers rather than from its own data.
- Problems surface through complaint. The first indication that something is wrong is a customer ringing, by which point the cost is already incurred.
- Monthly reporting is historical. A trend visible in a monthly report has been running for weeks, and the window to fix it cheaply has closed.
- Nobody watches between reports. The data exists continuously but is examined periodically, so gradual drift is invisible until it is large.
- Site differences unnoticed. One branch performs materially worse than the others and nobody compares them systematically.
What we deliver
Continuous monitoring of your key measures against a learned normal, alerts routed to the person who can act, and a short daily summary so the picture is visible without asking.
Continuous monitoring of your key operational measures
Alerts routed to the person who can actually act
A learned baseline of what normal looks like for you
A short daily summary of the operational picture
Site and team comparison so outliers are visible
Threshold tuning so alerts stay rare and meaningful
Features
- Rare alerts by design. Thresholds are tuned hard, because an agent that cries wolf gets muted and a muted agent is worse than none at all.
- Normal learned from your history. Seasonality and weekly rhythm are learned, so a quiet Friday afternoon does not generate an alert.
- Routed to somebody who can act. Alerts go to the person able to do something rather than to a general address nobody owns.
- Cross site comparison. Branches and teams are compared systematically, which is how persistent underperformance becomes visible.
- Works with data you already have. It uses your existing systems and spreadsheets rather than requiring new instrumentation.
Benefits
- Problems caught while cheap. A supplier slipping is addressable in week one and expensive by week four.
- Reporting stops being the only signal. Monthly reports become confirmation rather than discovery, which changes what they are for.
- Underperformance becomes visible. A branch consistently behind the others surfaces through comparison rather than through eventual complaint.
- Alerts get taken seriously. Because they are rare, they are read, which is the only version of monitoring that works.
Technologies we use
- Data connection. Reading from your ERP, point of sale, dispatch or spreadsheets without requiring new systems.
- Baseline learning. Establishing normal ranges from your own history, including weekly and seasonal patterns.
- Anomaly detection. Identifying genuine departures from normal rather than ordinary variation.
- Alert routing. Directing each alert to the role able to act on it, with escalation if it is not acknowledged.
- Comparison reporting. Ranking sites and teams on the same measures so outliers are unambiguous.
Alert discipline is the whole design. Every additional alert reduces the attention paid to all of them, so the bar for raising one has to be genuinely high.
How the process works, step by step
Operations monitoring usually takes four to six weeks, with a silent period where alerts are generated but not sent.
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Step 1
Free measure review
We establish which measures actually matter to your operation and which are merely available.
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Step 2
Data connection
We connect the systems and spreadsheets holding those measures, without asking you to instrument anything new.
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Step 3
Silent running
Alerts are generated but not sent for a fortnight, so the volume and quality can be judged before anybody is disturbed.
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Step 4
Threshold tuning
Levels are adjusted from that silent period until alerts would have been rare and each one justified.
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Step 5
Live with routing
Alerts go live to named people, with escalation where they are not acknowledged.
Quality assurance and how we work
The commitments attached to a system that interrupts people.
- Alerts are rare by design. Thresholds are tuned hard. An agent that cries wolf gets muted within a fortnight, and a muted agent is worse than no monitoring at all.
- Silent running before anybody is disturbed. We generate alerts without sending them for two weeks, so volume is judged before it interrupts anybody.
- Alerts reach somebody who can act. Routing goes to the role able to do something, not to a shared address that nobody owns.
- Normal is learned, not assumed. Your weekly and seasonal patterns are learned from history, so ordinary quiet periods do not trigger anything.
- We report the alerts you ignored. Unacknowledged alerts are reported back, because a pattern of ignored alerts means the thresholds are wrong.
Frequently asked questions about operations agent
Will we get constant alerts?
No, and that is the main design constraint. Thresholds are tuned hard because an agent that cries wolf gets muted within a fortnight.
Why is a muted agent worse than none?
Because everybody believes monitoring is in place while nothing is actually being watched, which is a false sense of coverage.
What data does it need?
Whatever you already record, from your ERP, spreadsheets, point of sale or dispatch system. It does not require new instrumentation.
What is silent running?
Alerts are generated but not sent for a fortnight, so we can judge volume and quality before anybody is interrupted.
How does it know what normal is?
It learns from your own history, including weekly rhythm and seasonality, so an ordinarily quiet Friday does not trigger an alert.
Who receives the alerts?
The role able to act on that specific measure, with escalation if the alert is not acknowledged.
What if we ignore alerts?
Unacknowledged alerts are reported back to you, because a pattern of ignoring them means the thresholds are wrong rather than the staff.
Can it compare our branches?
Yes, on the same measures, which is usually how persistent underperformance at one site finally becomes visible.
How long before alerts go live?
Four to six weeks including the silent period, which is the part we will not shorten.
Does it predict problems or just detect them?
It detects departures from normal early, which usually gives days rather than hours. Genuine prediction is a separate and much harder claim.
Can it monitor supplier performance?
Yes, and a supplier taking gradually longer is one of the most valuable things it catches, because nobody notices it manually.
Does it work with spreadsheet data?
Yes. Where the measure genuinely lives in a spreadsheet, we read it there rather than insisting on a system first.
What if our operation is seasonal?
Seasonality is learned from history, provided the history covers full cycles. Where it does not, we say so rather than alerting on every seasonal shift.
Can we set our own thresholds?
Yes, and you should. We propose levels from the silent period and your team adjusts them.
Does it replace our reporting?
No. Reports become confirmation rather than discovery, which is a better use for them.
How many measures can it watch?
As many as matter, though we start with a small number deliberately. Watching everything produces noise rather than insight.
Will it work across several sites?
Yes, and multi site operations usually gain most because comparison reveals what a single site view cannot.
What happens when it is wrong?
A false alert is logged and the threshold reviewed. Repeated false alerts on one measure mean the baseline needs work.
Is our operational data secure?
It stays in your systems, is read rather than copied where possible, and is never used to train public AI models.
What does continuous monitoring cost?
The number of measures and data volume determine it, quoted against your own operation before anything is agreed.
Glossary of terms
Monitoring vocabulary in plain terms.
- Baseline
- The learned range of normal for a measure, including weekly and seasonal variation.
- Anomaly
- A genuine departure from normal, as distinct from ordinary day to day variation.
- Alert fatigue
- The point at which people stop reading alerts because too many have been meaningless.
- Threshold
- The level at which a departure from normal becomes worth telling somebody about.
- Escalation
- Passing an unacknowledged alert upward so it is not simply ignored.
- Leading indicator
- A measure that moves before the outcome you care about, giving time to act.
Related services in AI Agents
Talk to us about operations agent
Think of the last operational problem that cost you money. Ask whether the data would have shown it a week earlier, because it usually would have.
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