Overview
When designing or evaluating an agentic system, inventory the harness explicitly:
- Model: Which model performs the reasoning, and is the task appropriate for its capability and
cost?
- Instructions: What global rules and project-specific rules are loaded before work begins?
- Context: Which files, summaries, conversation history, and task artifacts enter the active
context?
- Tools: What can the agent read, write, execute, browse, query, or control?
- Memory: What can be persisted between turns or sessions, and how is it updated?
- Permissions: Which actions happen automatically, which require approval, and which are prohibited?
- Limits: How many turns, tool calls, or tokens can the loop consume before stopping or compacting
context?
- Verification: How does the agent determine whether an action worked?
This inventory makes failures easier to diagnose. If the agent does not know a project fact, the problem may be context. If it repeatedly asks the user to perform an action it could perform itself, the problem may be an undeclared capability. If it takes unsafe actions, the problem may be permissions or insufficient execution constraints. If quality falls during a long task, the problem may be context growth rather than raw model intelligence.