Track Token Usage Multiple Agents from One Control Plane

You need a single place to track token usage multiple agents that run on your agent runtime. The Agent Command Center streams per-execution logs that include token counts and cost estimates so you can monitor spend without leaving the control plane.

All configuration for tracking lives in one versioned config object. Changes to model parameters or autonomy levels remain traceable across every run.

Key Takeaways

• Stream token counts and cost estimates per execution in real time.
• Store all tracking settings in a single versioned config object.
• Route high-token actions through the approvals inbox for human review.
• Compare runs across agents using execution logs stored in your agent runtime.
• Align token thresholds with external compliance frameworks to reduce audit preparation time.

Configure Token Tracking in the Versioned Config Object

Edit the config object to define which metrics appear in every log stream. Specify the model, maximum tokens per call, and whether cost estimates are calculated after each step. Additional fields let you record the agent identifier, the schedule that triggered the run, and the autonomy level active at execution time.

• Set the primary model identifier.
• Define a token ceiling that triggers a warning in the log.
• Enable cost-estimate calculation using current pricing tables.
• Choose whether intermediate outputs include partial token counts.
• Version the entire object so prior tracking rules stay available for audit.
• Record the exact prompt hash to correlate usage with specific prompt revisions.
• Include a daily aggregate token cap that pauses new runs once reached.

Version Agent Config Object for Traceable Runs shows the exact fields required for consistent token reporting.

Stream Execution Logs with Token Counts

Your agent runtime pushes logs to the command center as soon as each step completes. The stream includes prompt tokens, completion tokens, and a running total for the full run. You can also surface the cumulative cost estimate and flag any run that crossed a configured warning threshold.

• Prompt tokens used by the system message.
• Completion tokens generated in the first response.
• Cumulative tokens after each tool call.
• Estimated cost based on the active model.
• Timestamp of the log entry for chronological sorting.
• Agent identifier and schedule name for quick filtering.
• Autonomy level active during the step.

Stream Agent Execution Logs Token Usage Per Run explains how to filter these logs by agent ID or date range.

Compare Token Usage Across Different Model Parameters

Run the same task with varied model settings to see which configuration stays under budget. The command center displays side-by-side results from the stored logs. This comparison helps teams decide whether to tighten temperature settings or switch models entirely.

Choose Model Parameters Agents on Your Runtime covers how to store these parameter sets in the same config object.

Set Autonomy Levels That Respect Token Limits

Lower autonomy when token usage tends to spike. The config object lets you tie autonomy level directly to observed token patterns. You can also schedule periodic reviews that automatically lower autonomy if average tokens per run exceed a rolling 30-day baseline.

• Level 1: every action requires approval regardless of token count.
• Level 2: actions under 800 tokens run automatically; higher counts route to the approvals inbox.
• Level 3: only actions exceeding 2000 tokens or involving external systems need review.
• Level 4: full execution allowed when total tokens stay below a daily cap.
• Level 5: restricted to read-only tools when weekly spend approaches the team budget.

Set Agent Autonomy Levels Schedules in Config Object lists the exact syntax for these rules.

Route High-Token Actions to the Approvals Inbox

Any step projected to exceed a defined token threshold lands in the approvals inbox before it touches your agent runtime. You can edit the prompt or cancel the run at that point. The inbox records the projected token total, the estimated cost, and the autonomy level that triggered the gate.

• Review the projected token total and cost estimate.
• Approve, reject, or rewrite the prompt in one interface.
• Log the decision alongside the original execution log.
• Maintain a full audit trail for compliance reviews.
• Attach a note explaining why the token count was accepted or reduced.

Route Agent Actions to Approvals Inbox for Controlled Execution details the gating logic.

Review Intermediate Outputs Against Token Budgets

Check partial outputs before the agent consumes the remaining token allowance. The command center shows token usage up to the current step. Teams commonly insert these checkpoints after every third tool call or after any external API request.

• Current prompt tokens consumed.
• Tokens still available before the cap.
• Estimated cost if the run finishes at the current rate.
• Option to shorten the prompt or switch models mid-run.
• Button to forward the partial output to a second reviewer.

Review Intermediate Outputs Agents Before Execution explains how to insert these checkpoints.

Maintain Live Visibility Across All Agents

The dashboard aggregates token data from every agent running on your agent runtime. Daily and weekly rollups help you spot trends before costs grow. Many teams export these aggregates monthly to compare against internal budgets and external benchmarks.

• Total tokens used by each agent in the last 24 hours.
• Percentage of runs that hit approval thresholds.
• Average cost per completed task.
• List of agents whose usage exceeds the team average.
• Trend line showing token consumption over the past 30 days.

Teams often align these metrics with the NIST AI Risk Management Framework to maintain consistent oversight of autonomous systems.

Establish Token Usage Baselines for Future Planning

Create baselines by averaging token consumption across similar agent types and task categories. These baselines become the reference point for setting new autonomy rules and for forecasting monthly spend. Update the baseline quarterly or after any major model change.

• Calculate median tokens for lead-qualification agents over the last 90 days.
• Identify the top 10 percent of runs by token count and review their prompts.
• Set a new warning threshold at 1.2 times the current median.
• Compare baselines before and after a config-object version change.
• Export baseline data as CSV for finance or compliance teams.
• Document assumptions used when calculating the baseline, such as average prompt length.

Reference ISO artificial intelligence standards when documenting how baselines support measurable governance of AI operations. The resulting data also feeds directly into the versioned config object so future runs inherit updated thresholds automatically.

Conclusion

Track token usage multiple agents by keeping all settings in one versioned config object and reviewing high-cost steps in the approvals inbox. Start by updating the config object with token ceilings and cost-estimate flags, then verify the first few runs in the execution logs.

Next steps:

• Add token ceilings to your existing config object.
• Enable the approvals inbox for any action over 1500 tokens.
• Review the last seven days of logs for agents that exceed budget.
• Compare two model parameter sets on the same task.
• Schedule a weekly export of token totals for finance review.
• Recompute baselines after the next model-parameter change.

FAQ

How often are token counts updated in the logs?

Token counts appear in the stream within seconds of each model call completing on your agent runtime.

Can I set different token limits for each agent?

Yes. Each agent references its own section inside the shared versioned config object.

What happens when a run exceeds the configured token ceiling?

The action routes to the approvals inbox so you can approve, edit, or cancel before further tokens are consumed.

Do cost estimates reflect current provider pricing?

The command center pulls the latest published rates from each model provider when it calculates estimates.

Can I export token data for external reporting?

Execution logs can be exported as JSON or CSV directly from the Agent Command Center interface.