Observability & Evaluation
AI Agent Monitoring Platform
Monitors agent health, failures and drift in production.
Observability & Evaluation
AI Agent Logging
Every AI agent action is recorded as a structured log entry, timestamped and searchable, without full step-by-step tracing.
When someone asks what your AI agent did last Tuesday, the answer should not mean digging through raw provider output. AI Agent Logging adds structured logging to your existing agent workflows, recording each action, its outcome and a timestamp in one searchable format. It works across providers. Logs live in your own storage, so your team can filter and review them anytime.
- Every agent action logged with its outcome and timestamp
- One consistent log format across all your model providers
- Searchable records your team can filter and review
$2,900
20–30 working days
Optional maintenance $249/mo
What it is
AI Agent Logging adds structured logging to your existing agent workflows, recording each action taken, its outcome, and a timestamp, in a searchable format rather than scattered across raw provider output. It is a straightforward record-keeping layer, not full execution tracing.
Why businesses buy it
Agents act on behalf of your business, so sooner or later someone asks what one did and when. A customer disputes an answer, a manager wants a record, or a reviewer needs evidence. Without logs, nobody can say. After this build, every action your agents take is recorded in a structured, searchable form, so questions get answered from the record instead of from memory. It is lighter than full tracing. The Scope Card sets the agents, systems and log configurations in writing before you buy, so the work is clearly defined.
Problems this solves
Agent activity is buried in raw provider output nobody can search
Agent Logging adds structured logs to your existing agent workflows. Each action, its outcome and a timestamp are recorded in a searchable format your team can query.
A customer asks what an agent did and nobody can answer
The log shows each action taken on their behalf and when. Your team can look it up in the admin console or written report rather than digging through provider files.
Full tracing feels like overkill for simple agent workflows
This is a straightforward record-keeping layer, not full execution tracing. It covers up to three AI applications with up to five configured controls, without heavy engineering.
A good fit if
You need a searchable record of what your agents did, action by action, for compliance or accountability purposes.
Your agents currently write to raw provider logs or console output, and you want structured entries you can filter and query.
You do not need to debug an agent's internal reasoning, just confirm what it did and when it did it.
You need log entries in a consistent format across several agents, even if those agents use different underlying models.
Not the right fit if
You need to see the reasoning and tool calls behind each step, not just the outcome. See AI Agent Observability, which traces the full internal detail.
You need production health and uptime monitoring for deployed agents, not a log of past actions. See AI Agent Monitoring Platform.
You need multiple agents coordinated in one workflow, not logs from an agent running alone. See AI Agent Orchestration System.
You need to track spend rather than actions. See AI Cost Monitoring.
How it works
- We add structured logging calls into your agent's code at each meaningful action, so every entry captures what happened, when, and which agent did it.
- Logging is built vendor-neutral, working the same way whether your agent runs on OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI.
- Log entries follow a consistent schema across every agent in scope, so filtering by action type, outcome or agent works the same way everywhere.
- Logs route to a tool such as Grafana or your existing logging stack, so your team searches them alongside the logs you already use.
- Configuration for what gets logged is kept in version control, so a change to logging detail is reviewed the same way as any other code change.
- We test the logging against your agreed scenarios, produce a test report, and hand over an admin guide plus a recorded session on searching the logs.
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 logging tooling will run.
- A technical owner who can point us to the actions worth logging.
- Written authorization for any testing of the instrumented agent code.
- The list of agents and systems that need structured logging added.
- 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 log format 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 logged agreed.
Weeks 1–2
Logging calls added to agent code and connected to your chosen providers.
Week 3
Log routing and search set up, with a consistent schema across every agent.
Weeks 3–4
Test report produced by running logging against agreed scenarios.
Week 4
Your review and two rounds of changes to log format or retention.
Go-live
Live, recorded handoff session, and the 30 days of support begins.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $2,900 |
| Express | 15–25 working days | $3,400 |
| Rush | 10–20 working days | $3,900 |
What you receive at handover
- AI Agent Logging configured and deployed on your existing agent workflows, across up to three AI applications
- Structured, searchable logs of each action, outcome and timestamp
- Up to five controls, such as retention or alert rules, built from your policies
- 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 it quick to answer what an agent did and when.
- Helps support and engineering teams work from the same record.
- Adds accountability to agents without a large tracing project.
- Time to answer an agent activity question
- Time a sample of lookups before launch and repeat with similar questions after.
- Actions logged
- Compare logged actions with expected activity each week to confirm coverage.
- Support cases needing engineering help
- Count agent-related cases escalated to engineering, the month before and after launch.
Before you start: Try to answer a recent question about an agent's actions and note how long it takes. List where agent output is stored today.
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 logging afterwards costs roughly $20 to $300 a month in your own accounts, mostly hosting and the log storage tools that hold the entries.
AI model usage during build and testing also bills to your own provider accounts, so ownership of every provider relationship stays with your team.
Ask about running costsCustom requirements
If you need logging across more than three connected systems, or more than five log 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 log field 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 | A logging platform charges per gigabyte or per seat | A custom logging build is quoted after discovery | Ours is one fixed price, shown on this page before you buy |
| Scope | A logging platform leaves you to instrument each agent yourself | An agency agrees scope during the project | Ours is written to your agents and actions before you order |
| Delivery | A logging platform still needs real instrumentation before it is useful | An agency build usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Format | A generic logging tool leaves the schema to you | An agency deliverable depends on what was agreed | Ours uses one consistent schema across every agent |
Questions about this product
How much does AI Agent Logging 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: structured logging added to agents across up to three connected systems, up to five log configurations, storage set up in your own tools, and an admin guide with a recorded handoff session.
What does AI Agent Logging cost to run each month?
Running the logging afterwards costs roughly $20 to $300 a month in your own accounts, mostly hosting and the log storage tools that hold the entries. There is no separate platform license on top of that figure. The exact cost depends on how many agents you log and how much activity they generate.
How is AI Agent Logging different from AI Agent Observability?
AI Agent Logging is a narrower, structured record of what happened: the action, the outcome and a timestamp. AI Agent Observability goes further, tracing every prompt, tool call and reasoning step behind each action. If you only need to confirm what an agent did, without debugging why, the logging build is the simpler fit.
Can AI Agent Logging cover agents running on more than one AI provider?
Yes. Logging is built vendor-neutral, so entries follow the same consistent format whether an agent runs on OpenAI, Google Gemini, AWS Bedrock or Azure OpenAI. That consistency matters most when different agents in your stack use different underlying models but still need to be searched and filtered together in one place.
What access does AI Agent Logging need to my code?
We need to add logging calls into your agent's code directly, so a technical owner who can point us to the actions worth logging speeds up the build. 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 testing authorization.
Can we log more agents or change the log format later?
Yes. Adjusting what an existing log entry captures, or adding a field to an existing agent's logs, usually fits inside the monthly change hours that come with maintenance. Logging 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 Logging maintenance later?
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 logging needs stay light, you can decide whether you still need it.
Give every agent action a searchable record.
Order AI Agent Logging and structured, searchable logs across your agents are live on a fixed delivery date.
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