AI Agent Monitoring & Maintenance
This is ongoing observability and maintenance for AI systems already in production — yours or someone else's. Quality tracked against real traffic, cost per interaction visible, failures surfaced before customers report them, and model upgrades handled without breaking behaviour that currently works.
Who it's for
Anyone running AI in production without knowing if it is still working well.
What changes
You find out about quality drift before your customers do.
- Starting at
- ₹75,000
- Timeline
- 2–4 weeks for an audit
- Category
- AI Consulting & Enablement
- Built from
- Vashi, Navi Mumbai
Key takeaways
- AI systems degrade silently — nothing errors, the answers just get worse.
- Cost drift is common and usually invisible until an invoice arrives.
- Evaluation against a fixed test set is what makes model upgrades safe.
- We take on systems built by other vendors, and often do.
- From ₹40,000 a month depending on system count and traffic.
Why these systems fail quietly
Conventional software fails loudly. A broken endpoint returns an error and somebody's monitoring goes red.
An AI system that has degraded returns a perfectly well-formed answer that happens to be worse than it used to be. No error is raised, no alert fires, and nobody notices until customer complaints accumulate or a metric moves for reasons nobody can explain.
The causes are ordinary: a provider updated the model behind an endpoint, your product data changed, customers started asking about something new, a prompt was edited by someone who did not test it. All invisible without deliberate measurement.
What gets monitored
Four categories, each catching a different failure mode.
| Dimension | What is tracked | What it catches |
|---|---|---|
| Quality | Scored sample of real interactions | Gradual degradation |
| Cost | Per request, per user, per feature | Drift and runaway usage |
| Latency | Response time distribution | Provider slowdowns |
| Failure | Errors, refusals, escalations | Broken integrations |
| Coverage | Questions the system could not handle | Content and capability gaps |
| Safety | Policy violations, unexpected outputs | Reputational risk |
Evaluating quality without reading everything
You cannot review every conversation, so the measurement has to be a defensible sample rather than an impression.
We maintain a fixed evaluation set — a few hundred real inputs with known-good outputs, drawn from your actual traffic — and run it regularly. A drop in scores against that set is a hard signal rather than someone's sense that answers seem worse lately.
Alongside it, a random sample of live interactions is scored automatically and a smaller portion reviewed by a person. The automatic scoring catches trends; the human review catches the failures that look fine to a scoring model.
Cost, which drifts more than people expect
AI running costs move for reasons unrelated to any decision you made. Conversations get longer, context grows as documents are added, a new feature routes more traffic to an expensive model, one customer starts using the system very heavily.
We track cost per interaction, per feature and per customer, with alerts on unusual movement rather than a monthly total that arrives too late to act on.
Optimisation follows from the visibility. Routing simpler requests to cheaper models, caching repeated queries, trimming context that is not contributing — each is straightforward once you can see where the money goes, and together they often cut spend substantially without any quality change.
Taking on systems we did not build
A good share of this work is systems built by other vendors, or by a developer who has since left.
The engagement starts with an assessment: what it does, how it is built, what it costs, where it is fragile, and what is missing. That assessment is honest about the original build, including when it was done well — we are not looking for reasons to rebuild.
Sometimes the finding is that the system needs rework before it is worth maintaining. Sometimes it is well-built and simply has no monitoring, which is the most common case and the easiest to fix.
How the engagement works
From ₹40,000 a month for a single system at moderate traffic, scaling with system count and volume.
It covers continuous monitoring, monthly quality reporting, cost optimisation, prompt and configuration maintenance, model upgrade evaluation and migration, and a defined response commitment when something breaks.
The monitoring infrastructure runs on your systems and is yours. If the arrangement ends, you keep the dashboards, the evaluation set and the history — we are not holding the visibility hostage.
FAQ
AI Agent Monitoring & Maintenance — your questions
Our system is working fine. Why would we need this?
Because you would not currently know if it stopped. Most clients who come to us for monitoring do so after an incident — a chatbot that had been quoting a discontinued price for six weeks, a support agent whose escalation rate had doubled. The question worth asking is: if quality dropped 20% tomorrow, how would you find out and how long would it take? If the honest answer is customer complaints, that is the gap.
Can we do this ourselves?
Yes, and some clients should. If you have a technical person who can own it, we will build the monitoring infrastructure and evaluation framework as a one-off project and hand it over — typically ₹2,50,000 to ₹4,00,000 depending on complexity. The ongoing engagement makes sense when you do not have that person or when their time is better spent elsewhere. We are happy either way and will say which we think fits.
What happens when a provider deprecates a model?
We handle the migration, which is the main reason clients value this. Providers give notice, we evaluate the replacement against your evaluation set, adjust prompts where behaviour differs, test, and migrate with a rollback path. Done properly it is invisible to your users. Done as a rushed switch on the deprecation date, it usually is not, and that is the situation this arrangement is designed to avoid.
Do you need access to our production systems?
Read access to logs and metrics, and the ability to deploy configuration changes through whatever process you use. We do not need broad production access and would rather not have it. For clients with strict controls, everything can run through your own deployment pipeline with our changes reviewed by your team, which slows things slightly and is entirely reasonable.
How quickly do you respond when something breaks?
Defined in the agreement rather than left vague — typically four working hours for something degrading quality and one hour for a system that is down, with the option of tighter commitments where the system is customer-facing and critical. Much of the value is that alerting reaches us before you notice, so a meaningful share of incidents are resolved before anyone on your side has raised anything.
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Next step
Want a AI Agent Monitoring & Maintenance for your business?
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