Choose Model Parameters Agents on Your Runtime

Developers need a single place to select model parameters for every agent they run. The Agent Command Center stores those choices in one versioned config object so changes remain traceable across executions on your runtime backend.

Key takeaways:

All parameters live inside the same config object as prompts, tools and approval rules.
Human approval gates every action that touches the real world.
Token usage and cost estimates stream live during each run.
Version history shows exactly which parameter set produced which output.

Steps to Choose Model Parameters Agents

Follow this sequence when you edit an agent:

Open the config object for the target agent.
Select the base model and set temperature, top-p and max tokens.
Define autonomy levels that determine when the agent pauses for approval.
Save the version and assign it to the next scheduled run.
Review the streamed token usage before the agent executes further work.

When you follow these steps the runtime records every value against the new version identifier. This record lets you trace later outputs back to the exact parameter combination that generated them. Teams that skip the version step often lose visibility when multiple developers edit the same agent.

Parameters Stored in the Single Config Object

The config object holds every setting that affects output quality and cost. Keep these values together so you can roll back or compare runs.

Temperature controls randomness.
Top-p sets nucleus sampling threshold.
Max tokens limits response length.
Frequency penalty reduces repetition.
Presence penalty encourages new topics.
Stop sequences define early termination points.

Store these values alongside role prompts and tool definitions so every change stays in one place. Teams that separate parameters from the rest of the configuration often introduce drift between prompt intent and actual model behavior. The single object prevents that separation. Review the NIST artificial intelligence resources for background on parameter selection practices that support controlled deployment.

Compare Temperature and Top-p Trade-offs

Setting	Low Value Effect	High Value Effect	Typical Use Case
Temperature	More deterministic output	More varied output	Factual tasks vs creative tasks
Top-p	Narrow token selection	Broad token selection	Precise answers vs exploratory answers
Max tokens	Shorter responses	Longer responses	Quick summaries vs detailed reports

Use the table to decide values before you commit the version. Low temperature paired with low top-p produces repeatable results that are easier to approve. Higher values increase the chance of novel phrasing but also raise the risk of factual drift that later requires human correction.

Align Parameters with Tool and Schedule Settings

Model parameters interact directly with the tools and schedules defined in the same config object. A high temperature setting may produce tool calls that exceed the autonomy level you assigned, forcing an unplanned stop at the approvals inbox.

Match temperature to tool complexity: lower values for database writes, higher for search queries.
Adjust max tokens when a schedule runs during peak hours to control cost estimates.
Test stop sequences against tool output formats so the agent does not truncate structured data.
Re-validate autonomy levels after any parameter change that affects output length.

These alignments keep execution predictable across repeated runs on your runtime backend.

Set Autonomy Levels Alongside Parameters

Autonomy levels decide when an agent can act without review. Pair each level with the model parameters you just chose.

Level 1: read-only queries, no approval required.
Level 2: internal actions, log only.
Level 3: external writes, route to approvals inbox.
Level 4: financial or customer-facing steps, require explicit human sign-off.

Route Agent Actions to Approvals Inbox for Controlled Execution shows how to map these levels to your runtime backend. Parameter changes at level 3 or 4 must be tested on a dry run before the version is promoted to production schedules.

Monitor Token Usage After Parameter Changes

Every run streams token counts and cost estimates. Compare the numbers against the previous version of the config object.

Record baseline token usage before editing parameters.
Note the delta after each new version.
Set alerts when usage exceeds a chosen threshold.
Export logs for audit trails.

Stream Agent Execution Logs Token Usage Per Run explains how to keep this data visible without leaving the Agent Command Center. Stanford HAI research on model efficiency provides additional context for interpreting token deltas across different base models.

Version the Config Object After Every Edit

Versioning keeps parameter choices traceable. You always know which temperature or max-token setting produced a given output.

Increment the version number on save.
Add a short note describing the parameter change.
Tag versions that passed approval testing.
Roll back to an earlier version if token usage spikes.

Version Agent Config Object for Traceable Runs covers the exact steps. Each version also stores the autonomy level and schedule that were active at the time of the change.

Review Intermediate Outputs Before Execution

Model parameters affect what the agent produces mid-run. Route those outputs to a human reviewer when autonomy level 3 or 4 is active.

Inspect the partial response for accuracy.
Edit the output if needed before final approval.
Reject the run and adjust parameters if the tone drifts.

Review Intermediate Outputs Agents Before Execution describes the inbox workflow. Intermediate outputs become especially important when max tokens is raised, because longer generations increase the surface area for errors that need correction.

Evaluate Parameter Impact on Output Quality

After you save a new version, run the agent against a fixed test prompt set. Compare outputs from the prior version to the current one using the same input data.

Measure factual consistency on three to five representative prompts.
Count the number of tool calls that required manual edits.
Record any increase in approval rejections.
Note changes in average token usage per prompt.

These measurements reveal whether the parameter shift improved or degraded reliability before the agent handles live work.

Checklist for Safe Parameter Updates

Confirm the new config version before scheduling the next run.
Verify that sensitive actions still land in the approvals inbox.
Check live token usage against your budget.
Test the updated parameters on a small batch first.
Document the reason for each change in the version note.

Conclusion

Choose model parameters agents by editing the single versioned config object, then test the results with human approval in place. Start by opening an existing agent in the Agent Command Center at https://runagents.pro and adjust one parameter set today.

Next steps:

Open the config object for one active agent.
Update temperature or max tokens and save a new version.
Watch the first run with live logs and cost estimates.
Route any external action through the approvals inbox.

FAQ

How many parameters can one config object hold?

One config object stores temperature, top-p, max tokens, frequency penalty, presence penalty, stop sequences and the chosen model in a single file.

Do parameter changes require new approvals?

Any change to autonomy levels or actions that touch the real world still routes through the approvals inbox even after you update parameters.

Where do I see token usage for a specific version?

Token counts and cost estimates stream per execution and attach to the version that produced them inside the Agent Command Center.

Can I compare two versions side by side?

Yes. The version history shows every saved config object with its parameters, approval status and observed token usage.

What happens if token usage exceeds the limit?

The runtime pauses the agent and surfaces the run in the approvals inbox so you can adjust parameters or cancel the execution.