GPT-5.6 Sol for Long Agent Loops: A Practical Guide

#1 · Start with a small, verifiable task

A long agent loop repeatedly asks a model to choose an action, receives the result, and decides what to do next. With GPT-5.6 Sol, start by designing that loop carefully rather than assuming the model can run unattended. Confirm the model’s current availability, supported features, and exact API identifier before implementation.

Choose a first task with an obvious finish line: inspect a small repository, propose a documentation fix, and run the relevant checks. Avoid an opening instruction such as “improve the whole project.” Write down what success means and which actions require your approval.

#2 · Connect through a familiar client

Relay365 fits when you want pay-as-you-go access to multiple model families through a relay desk rather than maintaining separate integrations. It works with Claude Code, Codex, Gemini CLI, and OpenAI SDK clients. Its API base is https://api.api365relay.com; OpenAI clients add /v1.

Keep your API key in an environment variable, not in source control or prompts. Configure the client’s base URL and use the exact model identifier listed by the service. Run one simple request first. Then test any required tool-calling or structured-output features individually; client compatibility does not establish that every model supports every feature.

#3 · Give the agent a narrow operating contract

Your initial instruction should define the goal, available tools, boundaries, and completion criteria. For the documentation task, allow reading project files and running an approved test command. Require approval before deleting files, installing dependencies, accessing external services, or publishing changes.

• Goal: Correct one documented setup issue.
• Evidence: Identify the relevant files and report check results.
• Boundary: Do not modify credentials or unrelated code.
• Finish: Produce a reviewable patch and a short summary.

Enforce these boundaries in your application. Instructions alone are not an authorization system.

#4 · Build the loop around observable actions

Use a simple sequence: send the current task and state, inspect the response, validate any requested tool call, execute it, and return the result. If the model reports completion, verify the result before accepting it.

Do not execute arbitrary shell commands merely because the model requested them. Validate tool names and arguments against an allowlist. Apply timeouts, restrict filesystem access, and limit tool-output size. Treat repository files, web pages, and tool responses as untrusted data, not as instructions that can override your operating contract.

#5 · Save progress outside the conversation

Long loops become fragile when the transcript is their only memory. After each meaningful milestone, save a checkpoint containing the goal, completed work, modified files, verification results, unresolved questions, and next action.

Keep the original task and important constraints alongside a concise progress summary. When the conversation grows, rebuild the next request from that durable state instead of carrying every raw log forward. Preserve references to full logs when they may be needed for debugging. Review summaries for missing constraints before relying on them.

#6 · Set budgets and stopping rules

Before starting, define maximum iterations, elapsed time, tool executions, and spending. Enforce these limits in the runner, and check usage as it becomes available. Leave headroom because reported usage and billing information may not arrive immediately.

Add a no-progress rule: if the same error repeats without new evidence, pause and ask for help. Stop when verification succeeds, when approval is needed, or when a budget is reached. A useful long loop is one that can stop predictably, not one that continues indefinitely.

#7 · Recover without repeating side effects

Distinguish a failed model request from a failed tool action. Retry transient request failures with bounded backoff, but inspect the actual state before repeating an action that may have succeeded. Use idempotency keys where supported and record action identifiers. After a restart, resume from the latest checkpoint and recheck the workspace.

#8 · Test locally, then expand gradually

Run your first loop in a disposable workspace and inspect every action. Deliberately test a timeout, malformed arguments, a failed check, and a restart. Measure completion quality, cost, and the human effort needed to recover.

If a shared relay fits your workflow, create a Relay365 account and begin with a small, capped trial. Expand task scope only after the loop demonstrates reliable verification, clear checkpoints, and safe stopping behavior.