Observability & Evaluation
AI Agent Monitoring Platform
Monitors agent health, failures and drift in production.
Observability & Evaluation
AI Agent Observability
Every step, tool call and decision inside your AI agents is traced, so engineers see why an agent acted.
When an AI agent does something strange, a log line saying it ran is not much help. AI Agent Observability traces every step inside each run: the prompt sent, the tool called, what the model returned and the action it chose next. Engineers can follow the full chain and find the cause. Traces live in your own tooling, readable by your team.
- Every prompt, tool call and model response traced per run
- Multi-provider runs stitched into one readable trace
- Traces stored in your own tools, searchable by your team
$4,500
20–30 working days
Optional maintenance $249/mo
What it is
AI Agent Observability traces every step an agent takes internally: which tool it called, what prompt it sent, what the model returned, and why it chose the next action. It gives your engineering team the detail needed to debug unexpected agent behavior, not just a record that it ran.
Why businesses buy it
Debugging an agent without traces is guesswork. Engineers rerun conversations, add print statements and still cannot reproduce the odd decision a customer saw. That burns days and erodes trust in the agent. After this build, every run leaves a full trace of what the agent saw, did and decided, across whichever providers it used, so problems can be found and fixed with evidence. The Scope Card sets the agents, systems and trace configurations in writing before you order, so you know exactly what will be instrumented.
Problems this solves
An agent does something odd and nobody can see why
Agent Observability traces every internal step: the tool called, the prompt sent, the model's reply and the reason for the next action. Engineers can follow the agent's reasoning.
Logs show that an agent ran, but not what it did
Traces record the full sequence of decisions, not just a start and finish. Your team gets the detail needed to debug unexpected behavior across up to three AI applications.
Bugs in agent behavior take days to reproduce
Each trace keeps the exact prompts and responses involved. Engineers can study the failing run directly in the admin console or report instead of trying to recreate it.
A good fit if
An agent has done something unexpected in production and your team needed to guess why, because the reasoning behind each step was not visible.
You are actively developing or tuning agent prompts and need step-by-step traces to see what changed between versions.
Your team debugs agents by reading raw provider logs today, and wants structured traces of tool calls and decisions instead.
You need to reproduce a specific agent run, step by step, to explain an outcome to a teammate or a customer.
Not the right fit if
You only need a record that an agent ran and what it did, not full internal step tracing. See AI Agent Logging, our narrower structured-logging build.
You need production uptime and health monitoring across deployed agents, not developer-facing debugging detail. See AI Agent Monitoring Platform.
You need to coordinate multiple agents working together, not trace one agent's internal reasoning. See AI Agent Orchestration System.
You need to evaluate output quality against benchmarks, not trace execution steps. See AI Evaluation System.
How it works
- We instrument your agent's code to capture every step: the prompt sent, the model and provider used, each tool call, and the reasoning behind the next action taken.
- Traces are built vendor-neutral, working with OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI, and stitched together in tools such as Langfuse so one run reads as one trace.
- Usage, latency and cost are captured at each step, not just for the run as a whole, so a slow or expensive step is visible on its own.
- Traces are searchable by outcome, so your team can pull up every run where an agent chose a particular tool or produced a particular kind of answer.
- Configuration for what gets traced, and at what level of detail, is kept in version control, reviewed the same way as any other change to your codebase.
- We test tracing against your agreed scenarios, produce a test report, and hand over an admin guide plus a recorded session on reading and searching traces.
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 agents run on today.
- Cloud or hosting access for wherever the tracing tooling will run.
- A technical owner who can walk through the current agent code with us.
- Written authorization for any testing of the instrumented agent code.
- The list of AI applications and agents that need to be traced.
- 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 can approve the final trace detail and retention rules.
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 traced agreed with your technical owner.
Weeks 1–2
Agent code instrumented and traces connected to your chosen providers and logging tools.
Week 3
Trace search and dashboards configured, with per-step usage and cost captured.
Weeks 3–4
Test report produced by running tracing against agreed scenarios.
Week 4
Your review and two rounds of changes to trace detail or retention.
Go-live
Live, recorded handoff session, and the 30 days of support begins.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $4,500 |
| Express | 15–25 working days | $5,300 |
| Rush | 10–20 working days | $6,100 |
What you receive at handover
- AI Agent Observability configured and deployed, tracing agents across up to three AI applications
- Step-by-step traces of tool calls, prompts, model responses and next-action choices
- Up to five configured monitors and an admin console or written report for reviewing traces
- A test report with a control test log for the agreed scenarios
- An admin guide and a recorded 60-minute handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Can make agent bugs much quicker to find and fix.
- Helps engineers understand why an agent chose an action.
- Gives your team confidence to change agents with evidence in hand.
- Time to find the cause of an agent bug
- Record diagnosis time for each bug ticket and compare the months before and after launch.
- Bugs closed as cannot reproduce
- Count these in your issue tracker each quarter, before and after launch.
- Repeat incidents
- Count incidents with the same root cause recurring, using your incident log.
Before you start: Note how agent bugs are investigated today and how long recent ones took. Count bugs closed without a clear cause last quarter.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build price is a one-time cost. Running the tracing afterwards costs roughly $20 to $300 a month in your own accounts, covering hosting plus a trace storage tool such as Langfuse or Grafana.
AI model usage during build, testing and afterwards 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 tracing across more than three connected systems, or more than five trace configurations, 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
We recommend monthly maintenance: monitoring, fixes and a monthly allowance of change hours, which covers a new trace point or an added agent as your setup grows. It starts after the 30 days of support and can be cancelled any month.
$249/mo (the minimum fee, 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 | An observability platform charges per trace or per seat | A custom instrumentation build is quoted after discovery | Ours is one fixed price, shown on this page before you buy |
| Scope | An observability platform leaves instrumentation to your own team | An agency agrees scope during the project | Ours is written to your agents and code before you order |
| Delivery | An observability platform still needs real instrumentation work first | An agency build usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Detail | A generic observability tool is not built for agent-specific reasoning steps | An agency deliverable depends on what was agreed | Ours traces every prompt, tool call and decision an agent makes |
Questions about this product
How much does AI Agent Observability cost?
The fixed price for each delivery speed is shown in the price and delivery panel on this page before you order. It covers everything on the Scope Card: instrumenting agents across up to three connected systems, up to five trace configurations, trace storage set up in your own tooling, and an admin guide with a recorded session on reading traces.
What does AI Agent Observability cost to run each month?
Running the tracing afterwards costs roughly $20 to $300 a month in your own accounts, covering hosting plus a trace storage tool such as Langfuse or Grafana. There is no separate platform license on top of that figure. AI model usage from providers such as OpenAI or Google bills separately, directly to your own accounts.
How is AI Agent Observability different from AI Agent Logging?
AI Agent Logging is a narrower, structured record of what an agent did: the action, the outcome and a timestamp. AI Agent Observability goes deeper, tracing every prompt, tool call and reasoning step inside each run. Choose Observability when you need to debug why an agent behaved a certain way, not just record that it acted.
Can AI Agent Observability trace agents across more than one AI provider?
Yes. Tracing is built vendor-neutral, so it works whether your agents call OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI, or a mix of several. Traces are stitched together into one readable run regardless of which provider handled which step, which matters most when different agents use different models.
What access does AI Agent Observability need to my code?
We need to instrument your agent's code directly, so a technical owner who can walk through the current setup with us speeds up the build considerably. We also need 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, plus written authorization to test the instrumented code.
Can we trace more agents or add detail to existing traces later?
Yes. Adjusting what an existing trace captures, or adding a new field to an existing agent's trace, usually fits inside the monthly change hours that come with maintenance. Tracing 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 Observability 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 to the next month. If your agents are stable and tracing needs are light, you can decide whether you still need it.
Who owns the trace data AI Agent Observability collects?
You do. Traces are stored in your own logging or observability tooling, such as Langfuse or Grafana, not a platform we control, so every trace stays visible and searchable by your own team after handoff. We deliver an admin guide and a recorded session on reading and searching traces, and nothing stays locked to us.
Stop guessing why an agent did what it did.
Order AI Agent Observability and step-by-step agent traces are live on a fixed delivery date.
Works well with
Observability & Evaluation
AI Model Evaluation Platform
Runs ongoing evaluations across models and prompts with regression alerts.
Observability & Evaluation
AI Model Performance Dashboard
Compares model quality, latency and cost across workflows.