Inference for AI agents: models, budgets and retries
An agent needs model access that can finish its task within a budget. Choose inference by testing the full workflow: model quality, tool calls, context, latency, data handling and total cost per useful result. Start with a small, non-sensitive task before connecting actions or money.
Gyblos is in development. Its public marketplace, apps and agent integration are not released.
By Gyblos · Reviewed
Define the task before choosing a model
Write down the useful result, the allowed tools and the conditions that require a human decision. A drafting agent and an agent that changes records have different consequences when a tool call is wrong. Model access alone does not define a reliable agent.
Use the same small task set and acceptance criteria for every candidate. Include an ordinary case, missing information, a tool error and a request the agent should refuse or escalate. Record failures as well as successes.
Check the exact model and interface
A familiar API format is a starting point, not proof that a model supports your workflow. Check the exact model version, engine, client and configuration together.
| Capability | What to test | What to record |
|---|---|---|
| Tool calls | The right tool, valid arguments and correct handling of its result. | Schema, validation failures, repeated calls and actions needing approval. |
| Structured output | Required fields and usable values, including refusal and incomplete output. | Validation and rejection rules; valid JSON alone is not factual correctness. |
| Context and output | A realistic history, tool results and enough room for the answer. | Input size, output cap, truncation and any context-management policy. |
| Streaming and latency | Partial output, interruption and time until a usable result. | Completion state, timeout policy and end-to-end task time. |
| Data and service | Who processes inputs and which upstream services are used. | Data policy, retention claims, availability and remaining unknowns. |
Keep tool permission separate from model output
A model can propose a tool call; the application decides whether to run it. Validate arguments, restrict which tools are available and require approval for consequential actions. Treat retrieved pages and tool output as untrusted data, not authority to change permissions or send private information.
A retry of a model call and a retry of an external action are different operations. If a tool may already have sent a message, changed a record or initiated a payment, reconcile that action before considering another attempt. A fresh model response does not undo the earlier action.
Set limits for the whole task
Choose a total spending ceiling, maximum model calls, input/output limits, concurrency and a time limit. Include separately charged tools and network costs where applicable. Keep the remaining budget consistent across concurrent work; independent workers must not each assume they own the full balance.
A token estimate or budget alert is not an enforced spending cap. Check what the actual client, provider and payment mechanism can stop, including outstanding requests. Verify how failures, cancellations and rounding are billed. History and tool results can grow on later calls.
- Define success and count completed useful tasks separately from model calls.
- Use the exact offer’s input/output units and billing rules.
- Stop new work when a limit is reached or the outcome is uncertain.
- Record incurred cost and outstanding authorization separately; reserved funds are not settled spend.
An illustrative agent task: count every call
Suppose one task makes 10 model calls, averaging 2,000 billed input tokens and 1,000 billed output tokens per call. Invented rates of 0.10 and 0.30 USDC per million tokens give 0.0005 USDC per call: 0.005 USDC for all 10 calls. Add 0.002 USDC of separately charged tools for the whole task: total 0.007 USDC.
Two additional model calls at those same illustrative averages add 0.001 USDC, making 0.008 USDC if the tool cost stays unchanged. This is arithmetic, not a Gyblos quote, benchmark or guaranteed upper bound. Real context, tool charges and billing may differ.
If one useful task finishes, the cost per completed task is the total. If none finishes, report zero successes and incurred cost; the cost per success is undefined. Use the worksheet to compare the same quality requirement, including failed attempts.
Decide what to do before retrying
Classify the state using evidence from your client, tool and payment records. A timeout alone does not establish that nothing ran or nothing was charged.
| Observed state | Next step | Before resuming |
|---|---|---|
| Confirmed failure before sending | Correct the local problem without assuming provider work occurred. | Verify nothing was sent, then check the remaining limits. |
| Explicit rejection | Inspect the reason; correct input or wait only when appropriate. | Confirm that another attempt is permitted and its cost is covered. |
| Partial stream or lost response | Stop automatic retries and check completion and billed usage. | Preserve evidence; a partial answer does not establish cancellation. |
| Unknown payment or external-action outcome | Stop new paid calls and duplicate actions; reconcile the original operation. | Use the relevant payment/action record or operator review; never clear a pending state just to retry. |
| Confirmed completion but unusable result | Count its cost and record the quality failure. | Try another attempt or model only within permission, budget and call limits. |
A control flow to review with your team
Task and permissions → select a tested model → check remaining limits → send one allowed operation → validate the result → record cost and completion → finish, stop or reconcile before continuing.
This is a conceptual workflow, not executable code or a released Gyblos API. Model fallback does not resolve an uncertain payment or external action. A payment budget also does not grant permission to use every tool.
Where Gyblos fits — in development
Gyblos aims to open access to intelligence by letting people produce it, sell it or give it away. The direct design connects a buyer’s local proxy to an independent seller. The planned proxy targets OpenAI- and Anthropic-compatible interfaces; streaming, tool calls and format compatibility need evidence for the exact released combination.
The payment design uses buyer–seller channels in USDC on Solana; inference runs off-chain. Reserved balance, pending authorization and settled spend are different states. Operational recovery, delivery protections and production availability remain separate validation work.
In the direct route the seller can read prompts. A declared policy is not proof of enforcement, and statistical model audits do not prove every answer correct. This website does not provide a live inference API, funded wallet, runnable MCP server or production A2A endpoint. Documentation and discovery are not execution.
A small evaluation you can prepare now
Keep one dated record per candidate: task criteria, model/client versions, call and token counts, useful results, elapsed time, costs and failure outcomes. Use public or synthetic inputs; publish aggregates and methods rather than prompts, responses, keys or customer records.
Choose only from access you can verify today. An allowance must fit your workflow and limits; a low per-token price does not establish a lower cost per useful result. Wait for a tested Gyblos release before treating examples as an installation guide.