Observability & Evaluation
AI Model Evaluation Platform
Runs ongoing evaluations across models and prompts with regression alerts.
Observability & Evaluation
AI Agent Monitoring Platform
Deployed agents are watched around the clock for uptime, health and anomalies, before a customer notices first.
When an AI agent stalls or starts erroring in production, the first person to notice is often a customer. This platform watches your deployed agents around the clock for uptime, health and unusual behavior, across every provider in scope, and alerts your team when something looks wrong. Thresholds are agreed with you, the dashboards run in your own accounts, and every response decision stays yours.
- Agent outages and error spikes flagged before customers report them
- One health view across agents on different model providers
- Usage, latency and cost metrics for every monitored workflow
$5,900
20–30 working days
Optional maintenance $295/mo
What it is
AI Agent Monitoring Platform watches your deployed agents in production for uptime, health and anomalies, giving a general-purpose answer to whether everything is running correctly right now. It is the broad, always-on watch, not a debugging tool or a narrow log of past actions.
Why businesses buy it
Once a business has several AI agents live, nobody can watch them all by hand. An agent that goes silent overnight, or starts failing one request in five, costs customers and trust before anyone opens a log. This platform gives you a steady, always-on answer to whether everything is running correctly right now, with alerts that point to the agent and the symptom. The scope is written down before you order: which systems are watched, how many monitors are set up and where alerts go, so there is no open-ended project to manage.
Problems this solves
Nobody notices an AI agent has stopped working until customers complain
The platform watches up to three of your AI applications in production for uptime and health. A stalled or failing agent shows up in the admin console or status report. Your team can act before customers raise it.
Each AI tool is checked separately, so problems hide between them
Instead of logging into each tool, your team gets one view across the agreed systems. Up to five monitors cover the signals that matter most to you, such as errors or unusual activity.
Odd agent behavior goes unseen because nobody defined what normal looks like
We turn your own policies, up to twenty documents, into the rules each monitor checks against. Anything outside those rules is flagged, giving a clear line between expected behavior and something worth a look.
Leaders cannot easily say whether the AI is running correctly right now
The admin console or written report gives a plain status for each monitored application. The control test log shows how each monitor performed against agreed scenarios, so the status it reports can be trusted.
A good fit if
You have agents running in production and want to know immediately if one goes down or starts behaving abnormally.
You currently find out about an agent problem from a customer or a colleague, rather than from a monitor.
You run several agents and need one place that shows whether all of them are healthy, not a separate check for each.
You need uptime and anomaly detection as a standing production concern, not a one-off debugging session after something breaks.
Not the right fit if
You need step-by-step tracing of one agent's internal reasoning to debug a specific issue. See AI Agent Observability, our developer-facing tracing build.
You only need a structured record of what an agent did, not real-time health monitoring. See AI Agent Logging.
You need multiple agents coordinated in one workflow, not health monitoring of agents already running independently. See AI Agent Orchestration System.
You need an AI system that actively supervises and can intervene in another agent's ongoing work. See AI Supervisor Agent.
How it works
- We connect to your deployed agents, typically running on OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI, wherever they operate in production today.
- Uptime, response health and error rate are tracked continuously for each agent, so a failure or a slowdown is visible the moment it starts.
- Anomaly patterns are learned against each agent's normal behavior, so a sudden change, not just a hard failure, is caught and flagged.
- Alert thresholds are agreed with you, so a genuine anomaly reaches the right person while normal fluctuation does not generate noise.
- Findings route to Slack, Grafana or a written report, wherever your team already watches production signals for other systems.
- We test the monitoring against your agreed scenarios, produce a test report, and hand over an admin guide plus a recorded session covering alert tuning.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- Your business information (company basics, hours and brand voice), shared during intake, or confirmation we can reuse what you gave us on an earlier order.
- The AI providers and API accounts your deployed agents run on.
- Cloud or hosting access for wherever the monitoring tooling will run.
- A technical owner who can confirm what counts as normal agent behavior.
- Written authorization for any testing of the monitoring connections.
- The list of agents and systems that need production monitoring.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- One decision-maker who signs off the final alert thresholds and routing.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
business information confirmed and the agents to be monitored agreed.
Weeks 1–2
Monitoring connections built and normal-behavior baselines agreed with your technical owner.
Week 3
Anomaly detection configured and alert routing to Slack, Grafana or a report set up.
Weeks 3–4
Test report produced by running monitoring against agreed scenarios.
Week 4
Your review and two rounds of changes to thresholds or routing.
Go-live
Live, recorded handoff session, and the 30 days of support begins.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $5,900 |
| Express | 15–25 working days | $6,800 |
| Rush | 10–20 working days | $7,650 |
What you receive at handover
- The monitoring platform configured and deployed in your environment, covering up to three AI systems or applications
- Up to five monitors and controls built from your policies, with their prompt and guardrail settings
- An admin console or written status report, depending on the agreed setup
- A control test log showing how each monitor behaved in the agreed scenarios
- An admin guide and the recording of the handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Problems with live agents can surface sooner, before they reach customers or staff.
- Helps your team spend less time checking each AI tool by hand.
- Makes it easier to show management that AI systems are watched against agreed rules.
- Time to detect an agent failure
- Compare when issues were first spotted before launch, from tickets or incident notes, with alert times in the console after launch.
- Customer-reported AI faults
- Count support tickets that mention an AI tool fault in the month before launch and in each month after.
- Manual checking time
- Ask the people who check AI tools to log their hours for two weeks before launch and two weeks after.
Before you start: List the AI applications in scope and note how failures were found over the last few months. Record each incident and how long it went unnoticed.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build price is one-time. Running the monitoring afterwards costs roughly $20 to $300 a month in your own accounts, covering hosting plus the logging and monitoring tools, such as Grafana, that track agent health.
AI model usage that runs through the monitored agents is also billed directly to your own provider accounts, so ownership of every provider relationship stays with your team.
Ask about running costsCustom requirements
If you need monitoring across more than three connected systems, or more than five thresholds, add a note at checkout. You get a written Change Quote, and your original order keeps its normal timeline. Larger or bespoke projects use the Custom AI request form instead.
Request a custom versionKeep it running
For this build we recommend monthly maintenance: monitoring, fixes and a monthly allowance of change hours, which covers new alert thresholds or an added agent as your deployment grows. It starts after the 30 days of support and can be cancelled any month.
$295/mo (5% of the build price a month, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A monitoring platform charges a per-agent or per-seat subscription | A custom monitoring build is quoted after discovery | Ours is one fixed price, shown on this page before you buy |
| Scope | A monitoring platform leaves anomaly definitions to your own team | An agency agrees scope during the project | Ours is written to your agents and baselines before you order |
| Delivery | A monitoring platform still needs real configuration before alerts are trusted | An agency build usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Focus | A general uptime tool is not built for agent-specific anomalies | An agency deliverable depends on what was agreed | Ours watches uptime, health and anomalies specific to deployed agents |
Questions about this product
How much does AI Agent Monitoring Platform cost?
The build is one fixed price, listed with each delivery speed in the price and delivery panel on this page. It covers everything on the Scope Card, including up to three systems or AI applications watched, up to five monitors or alerts configured, and one dashboard or written report. Hosting and monitoring tools such as Grafana run in your own accounts and are billed to you directly.
What does AI Agent Monitoring Platform cost to run each month?
Running the monitoring afterwards costs roughly $20 to $300 a month in your own accounts, covering hosting plus the logging and monitoring tools, such as Grafana, that track agent health. There is no separate platform license on top of that figure. The exact cost depends on how many agents and systems you monitor.
How is AI Agent Monitoring Platform different from AI Agent Observability?
AI Agent Observability traces each agent's internal steps for developer-focused debugging: prompts, tool calls, reasoning. AI Agent Monitoring Platform is broader and production-facing, watching uptime, health and anomalies across deployed agents so you know quickly if something is wrong, without the step-by-step detail needed to work out exactly why.
Can AI Agent Monitoring Platform watch agents across more than one AI provider?
Yes. Monitoring is built vendor-neutral, so it works whether your agents run on OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI, or a mix of several. One dashboard shows uptime and health across every provider in scope, which matters most when different agents in production use different underlying models.
What counts as an anomaly that AI Agent Monitoring Platform flags?
Anomaly thresholds are agreed with your technical owner during setup, based on each agent's normal behavior, such as a sudden error spike, a response time far outside its usual range, or an agent going silent. The platform reports the anomaly with the detail behind it; deciding what to do about it stays with your team.
Can we monitor more agents or adjust thresholds later?
Yes. Adjusting an existing alert threshold, or refining what counts as normal behavior for one agent, usually fits inside the monthly change hours that come with maintenance. Monitoring a genuinely new agent, or a fourth or fifth connected system beyond the three in scope, goes through a Change Quote agreed before that work starts.
Can we cancel the AI Agent Monitoring Platform maintenance?
Yes. Maintenance has no lock-in, so you can cancel any month once the 30 days of included support has ended. Unused hours do not roll over. If your agent deployment is stable, you can decide whether you still need it.
Know an agent is down before a customer does.
Order AI Agent Monitoring Platform and round-the-clock watching of your deployed agents is live on a fixed delivery date.
Works well with
Observability & Evaluation
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