Track time for AI agents by instrumenting your agent framework to log every task start, action taken, tool called, and task completion — then map those events to projects and clients for billing and cost control.
Your AI agents are working around the clock. They run research, generate code, process data, and execute multi-step workflows at 3am on a Sunday without asking for overtime. But unless you are tracking what they do, how long it takes, and what it costs, you have no visibility into your fastest-growing cost centre. According to a 2025 Deloitte study, 25% of enterprises expected AI agents to perform autonomous work by year-end. Yet most of these organisations had no system for tracking agent activity with the same rigour they apply to human timesheets.
Why Should You Track Time for AI Agents?
Five reasons make agent time tracking a business requirement, not a nice-to-have.
Cost control. AI agents consume compute resources, API credits, and third-party service calls. A single agent running a complex research workflow might cost £0.15 or £15, depending on the number of retries, tool calls, and tokens consumed. Without tracking, these costs are invisible line items buried in infrastructure bills. Teams deploying multiple agents report that untracked costs can account for 15-30% of total cloud spend.
Client billing accuracy. If your agents do work for clients — research, analysis, content generation, code review — you need data to bill for it. Accurate tracking provides the evidence: what was done, how long it took, and what it cost. This connects directly to billing for AI agent work.
Performance benchmarking. Tracking agent task duration over time reveals whether your agents are getting faster or slower, more or less reliable. A coding agent that completed pull request reviews in 4 minutes last month but now takes 12 minutes needs investigation — perhaps a model change, a prompt regression, or a tool integration failure.
Quality assurance. Error rates, retry counts, and incomplete tasks are all signals of agent quality. Without tracking, you only discover problems when a client complains or a cost spike appears on your bill.
Compliance and audit trails. Regulated industries need records of automated decisions. What data did the agent access? What actions did it take? What output did it produce? A complete time-stamped trail meets governance requirements for AI accountability.
What Metrics Should You Track for AI Agents?
Human time tracking captures one primary metric: hours. AI agent tracking captures a richer set of data because agent work is measurable at a much finer level.
| Metric | What It Measures | Why It Matters |
|---|---|---|
| Task duration | Wall-clock time from start to completion | Billing, performance benchmarking |
| Token usage | Input and output tokens processed | Cost attribution (tokens drive LLM cost) |
| API calls | Number of external service calls made | Cost, rate limit management |
| Tool invocations | Actions taken (search, write, execute, read) | Understanding agent behaviour |
| Cost per task | Total compute + API + token spend | Profitability analysis, client billing |
| Error rate | Failed actions / total actions | Quality assurance |
| Retry count | Number of repeated attempts per step | Efficiency measurement, cost control |
| Output quality | Success rate, human acceptance rate | Value delivered to client |
The most important metric for billing is cost per task — the total spend attributed to a single unit of work. For internal cost management, token usage and API calls matter most, as they drive the majority of agent operating costs.
What Are the Methods for Tracking AI Agent Time?
Three approaches exist, ranging from developer-built logging to purpose-built platforms.
Method 1: Application-Level Logging
Most agent orchestration frameworks include callback hooks or event systems that fire when actions occur. You can write custom code that captures timestamps, token counts, and action metadata at each step.
A typical implementation logs events like:
[2026-03-05T09:14:02Z] task_start: "Draft quarterly report" (project: client-a)
[2026-03-05T09:14:03Z] tool_call: retrieve_data (1.2s, 3,400 tokens)
[2026-03-05T09:14:08Z] tool_call: analyse_trends (4.8s, 12,100 tokens)
[2026-03-05T09:14:15Z] tool_call: write_section (6.1s, 8,900 tokens)
[2026-03-05T09:14:22Z] task_complete: success (total: 20s, cost: £0.12)Pros: Granular control. Developer-friendly. No external dependencies. Cons: Requires custom code for every agent. No built-in dashboards. Difficult to aggregate across multiple agents and projects.
This method works well for teams with a single agent in production. It breaks down when you run 5, 10, or 50 agents across multiple clients.
Method 2: Observability Platforms
AI observability platforms capture detailed traces of agent execution — every LLM call, tool invocation, and decision point. They are built for debugging and performance monitoring.
These platforms provide:
- Full execution traces with latency breakdowns
- Token usage and cost tracking per trace
- Error analysis and anomaly detection
- Comparison across model versions and prompt changes
Pros: Detailed technical insight. Strong debugging capabilities. Visualised execution traces. Cons: Developer-focused, not billing-oriented. No client/project mapping. No invoice generation. Designed for engineering teams, not finance or operations.
Observability platforms answer “how did the agent perform?” but not “how much should we bill the client?”
Method 3: Dedicated Time Tracking Platforms
Purpose-built platforms bridge the gap between technical observability and business operations. They track AI agent time alongside human time in a unified dashboard — mapping agent activity to projects, clients, and billing rates.
Pros: Unified human + agent view. Billing-ready reports. Client/project attribution. Invoice integration. Cons: Newer category. Fewer options than traditional time tracking tools.
Integration typically works through webhooks, REST APIs, or SDKs that connect to your agentic AI tools and orchestration framework. Events flow from your agent runtime into the tracking platform, where they are categorised, costed, and attributed to the correct project.
How Do You Set Up AI Agent Time Tracking?
Five steps take you from zero visibility to full agent tracking.
Step 1: Define what constitutes a “task.” A task is the smallest billable unit of agent work. For a research agent, a task might be “research competitor pricing.” For a coding agent, it might be “review pull request #142.” Be specific — vague task definitions make billing disputes inevitable.
Step 2: Instrument your agent framework. Add logging at three points in every agent workflow: task start, each action/tool call, and task completion. Capture timestamps, token counts, tool names, and success/failure status at each point. If your framework supports callbacks or middleware, use them rather than scattering logging throughout your code.
Step 3: Map agent tasks to projects and clients. Every task needs a project tag and a client tag — the same way every human timesheet entry does. Without this mapping, tracked data cannot be attributed for billing or cost analysis. Configure your logging to include project and client identifiers in every event.
Step 4: Set billing rates for agent work. Decide how you will charge for agent time. Options include cost-plus (agent cost + margin), reduced human-equivalent rate, per-task flat fee, or outcome-based pricing. Configure the rate in your tracking system so reports calculate billable amounts automatically.
Step 5: Create reporting dashboards. Build or configure dashboards showing: agent cost per project, agent cost per client, agent utilisation across your portfolio, and comparison of agent vs human cost for similar tasks. Review these weekly alongside your team’s human time reports.
What Are the Proven Approaches for AI Agent Time Tracking?
Six practices separate teams that manage agent costs from those surprised by them.
1. Track at the task level, not just the session level. A session might contain multiple tasks. If you only log “agent ran for 45 minutes,” you cannot attribute costs to individual projects. Log every discrete task with its own cost and duration.
2. Include human review time in agent task costs. Most agent output requires human review. A coding agent that generates a pull request in 3 minutes still needs 15 minutes of human review. The true cost of that task is agent cost + human review time. Track both together for accurate project costing.
3. Set cost alerts and budgets per project. Configure alerts when agent spend on a project exceeds a threshold — before it becomes a surprise on the monthly bill. One practitioner noted that AI-powered tools should give you “clear productivity insights in seconds, powered by real work data.” Cost alerting is the agent equivalent.
4. Compare agent efficiency to a human baseline. For every task an agent performs, know what the human alternative would cost. If an agent completes a research task for £0.20 that would take a human 2 hours at £150/hour, the ROI is clear. If an agent costs £8 for a task a human does in 15 minutes, reconfigure the agent.
5. Review and optimise weekly. Pull a weekly report of agent activity: total cost, tasks completed, error rate, and cost-per-task trends. Look for agents that are getting more expensive over time — this often signals prompt drift, model changes, or unnecessary retries.
6. Start simple, then graduate. Begin with application-level logging for your first agent. As you add more agents and clients, move to a dedicated platform that unifies agent and human tracking. Do not over-engineer the tracking before you have agents in production — but do not launch agents without any tracking at all.
Key Takeaway
Track AI agents at the task level with timestamps, token counts, and cost attribution — then map every task to a project and client for billing and cost control.
Track AI Agent Time in Minutes
Keito integrates with your agent framework to track every task, token, and minute automatically.
Frequently Asked Questions
How do you track time for AI agents?
Instrument your agent framework to log events at task start, each action, and task completion. Capture timestamps, token usage, API calls, and costs. Map each task to a project and client. Use application-level logging for simple setups or a dedicated tracking platform for multi-agent, multi-client environments.
What metrics should you track for AI agents?
Track task duration, token usage (input and output), API calls, tool invocations, cost per task, error rate, retry count, and output quality. Cost per task is the most important metric for billing. Token usage and API calls are the most important for cost management.
Can you bill clients for AI agent work time?
Yes. Define what agent activities are billable with clients upfront. Track agent tasks with the same detail as human timesheets — task name, duration, cost, and outcome. Choose a billing model (cost-plus, per-task, or outcome-based) and generate transparent reports showing what the agent did and what it cost.
What tools track AI agent time?
Three categories of tools exist. Application-level logging (built into orchestration frameworks) captures raw event data. Observability platforms provide detailed execution traces for debugging. Dedicated time tracking platforms map agent activity to projects and clients for billing, offering unified dashboards for both human and agent work.
How do you set billing rates for AI agent work?
Choose from four models: cost-plus (agent cost plus a margin), time-equivalent (bill at a reduced human rate for equivalent work), per-task flat fee (fixed price per deliverable), or outcome-based (bill based on the value of the result). The right model depends on client expectations and the type of work performed.