Limit Agent Runtime Resources in Config Object

You set resource limits directly in the config object that governs every agent on your runtime. This keeps token usage, memory bounds and execution windows under explicit control before any run starts.

The primary keyword limit agent runtime resources appears in the first paragraph because the config object is where you declare those constraints once.

Key takeaways

Resource limits live in one versioned config object
Token thresholds trigger alerts before approvals inbox review
Human approval gates every real-world action
Live logs show per-run token usage and cost estimates

Define Resource Limits Inside the Single Config Object

Open the config object for the agent you want to constrain. Add a resources section that lists maximum token counts, memory caps and wall-clock time. These declarations apply uniformly to every execution that uses that version.

Maximum input tokens per step
Maximum output tokens per step
Total memory allocation in megabytes
Maximum runtime duration in seconds
Priority level for queue ordering
Swap allowance flag for overflow handling

These values stay versioned with every prompt or tool change so you can trace which limit set produced a given outcome. Changes require a new config version before they affect subsequent runs.

Set Token Usage Thresholds That Feed Cost Estimates

Token limits directly shape the cost estimates shown in the approvals inbox. Configure a hard stop so an execution never exceeds the declared budget. This prevents surprise charges when an agent processes unusually large contexts.

Choose the model family in the config object
Enter the input-token ceiling
Enter the output-token ceiling
Add a cost-per-1k-tokens multiplier
Save the new version
Verify the resulting estimate appears in the next execution preview

Review per-run cost estimates before approvals inbox to catch overruns early. Thresholds can also trigger automatic alerts when usage reaches seventy percent of the declared ceiling.

Configure CPU and Memory Bounds for Your Agent Runtime

Your agent runtime backend respects the memory and CPU fields you declare. These bounds prevent a single agent from starving other workloads that share the same backend.

CPU shares expressed as millicores
Memory limit in megabytes
Ephemeral storage limit
Swap allowance flag

See how these settings compare with default runtime behavior in the table below. Kubernetes resource management guidelines provide additional context on container-level enforcement that your runtime backend mirrors.

Setting	Default Value	Recommended Limit	Effect on Execution
CPU shares	100m	250m	Prevents throttling under load
Memory	256Mi	512Mi	Avoids OOM kills on large contexts
Token input ceiling	None	8000	Caps prompt cost
Token output ceiling	None	2000	Caps response cost

Route Sensitive Steps Through Human Approval

Any action that touches the real world must reach the approvals inbox. Resource limits reduce the chance an unbounded run generates an expensive or unsafe request that would otherwise reach production systems.

Approval required for external API calls
Approval required for file writes outside the sandbox
Approval required when token usage crosses 70 % of limit
Approval required for network egress beyond approved domains

Review per-run cost estimates before approvals inbox to see the exact token counts that will be charged.

Monitor Live Logs and Intermediate State Changes

After you apply limits, watch execution streams for compliance. Logs report token counts at each step and surface any early termination caused by a breached ceiling. This visibility lets you adjust ceilings without guessing.

Step-level token consumption
Memory high-water mark
Wall-clock duration
Exit reason (limit reached or completed)
Intermediate output size at each checkpoint

Inspect intermediate state changes during agent runs to trace exactly where a limit took effect.

Compare Resource Controls Across Common Agent Tasks

Different tasks need different ceilings. Use the table below to choose starting values that reflect typical workload profiles.

Task Type	Input Token Limit	Output Token Limit	Memory Limit	Typical Duration
Data summarization	4000	1500	256Mi	45 s
Code generation	6000	3000	512Mi	90 s
Scheduled reporting	2000	800	128Mi	30 s
Research queries	8000	2000	384Mi	120 s

Assign scheduled work from the control plane to apply these limits to recurring jobs.

Validate Limits Against Model Selection Rules

Model choice affects both token cost and runtime speed. Pair resource limits with model selection rules stored in the same config object so the two constraints remain consistent across versions.

Map high-priority tasks to faster models
Map low-priority tasks to cheaper models
Keep both rules under one version tag
Re-test cost estimates after any model swap

Set model selection rules config in one object so limits and model choices never drift apart.

Audit Resource Usage Over Time

Export per-run metrics from the control plane to track whether declared limits remain realistic. Regular audits reveal patterns such as seasonal spikes or gradual drift in token consumption.

Average tokens consumed per execution
Peak memory observed
Number of runs terminated by limits
Cost delta after each config version
Frequency of approvals inbox interventions triggered by thresholds

Configure per execution cost alerts in control plane to receive notifications when usage trends upward.

Test Resource Limits in Isolated Runtime Environments

Before rolling new ceilings to production agents, run controlled tests on a dedicated slice of your agent runtime backend. This isolates the impact of each limit without affecting live schedules or approvals workflows.

Clone the production config object
Apply tightened resource values
Execute a representative sample of tasks
Compare token usage and exit codes against baseline runs
Document any required adjustments in the version notes

Control plane audit trails for step-level agent tracking help you replay these test executions side by side. Testing also surfaces interactions between resource limits and priority rules that only appear under load.

Conclusion

Apply resource limits in the config object first, then test with a single execution before scaling to the full fleet. The next steps below keep the process repeatable.

Open an existing config object
Add the resources section with concrete ceilings
Save as a new version
Run one test execution and review logs
Route the output through the approvals inbox
Adjust ceilings based on observed token usage

FAQ

How do I declare a token limit in the config object?

Add an integer value under the resources.token section. The runtime backend enforces the ceiling on every subsequent run.

What happens when an agent hits a memory limit?

The execution terminates immediately and writes an exit reason to the live logs. No partial output reaches the approvals inbox.

Can I change limits without affecting running agents?

Yes. New versions of the config object apply only to future executions. Existing runs continue under the version active at start time.

How do cost estimates reflect the limits I set?

The control plane multiplies declared token ceilings by the model price to produce the estimate shown before any approval decision.

Do resource limits replace the need for human approval?

No. Limits reduce risk but every action that touches production systems still requires explicit approval in the approvals inbox.