A free application can use paid models. A local model still consumes machine resources and review time. Plan an agent team around the complete job: coordination, execution, retries and evaluation. This guide proposes a tracking method; it does not describe a budgeting dashboard built into ConductLoop.
Separate the cost categories
Distinguish the application’s price, model-provider usage and external services you connect. ConductLoop is free under the MIT license and supports your own model key. Cloud-model costs depend on the provider’s current terms and your usage. A paid tool can add another charge. Do not group every expense under ‘tokens’, because that makes it difficult to identify which decision increased or reduced spending. Consult provider documentation for actual prices.
Measure the whole assignment
Track a task from its start until you accept the deliverable. Include coordinator calls, individual agents, failed attempts and revisions. A cheap run requiring a complete rewrite can be less useful than a more expensive run that produces an acceptable result immediately. Record the time you spend correcting it too. If exact task-level billing is unavailable, mark the number as an estimate and keep the method used to calculate it.
Use a simple tracking sheet
Assignment | Accepted result | Calls | Provider cost Retries | Review time | External tools | Notes
Compare tasks of the same type with similar acceptance criteria. The useful question is how much it costs to obtain an acceptable result. Do not compare a broad research assignment with a short classification as though they were equivalent. Begin with a few representative tasks and keep their inputs so the comparison can be repeated.
Avoid purposeless context repetition
Each role needs material that can change its decision. Sending every agent the complete history can increase processing and make relevant information harder to locate. Try handoff summaries preserving sources and decisions. Do not discard evidence solely to reduce size: an incorrect result caused by missing context is not a saving. Evaluate changes with the same assignment and observe both quality and consumption.
Local models also have constraints
With Ollama running locally, model calls are processed on your machine. Capacity depends on the model and available resources. Ollama’s context-length documentation explains that increasing the context window increases memory requirements. This matters when planning several tasks: more context or concurrency is not a free improvement. Measure latency and resource use under your actual workload before expanding the team.
Decisions to make before automation
Decide which jobs justify a team and which can be handled in one interaction. Propose an effort limit and a stopping condition for retries, even when those controls are initially manual. ConductLoop’s planned hierarchy uses a CEO coordinator; details of budgets, counters or automatic limits are not announced here. Documenting consumption alongside results will help you choose a sustainable workflow in the meantime.