The control pattern
Data asset
Tool, state and workflow data
Knobs
Available tools, Permissions, Memory, Planning depth, Stop conditions, Action budget
Outcome
Better multi-step decisions with controlled execution
Measurement
See how to measure this below
What it is
An agent is an AI system that can select tools, maintain state and take multiple steps toward a goal. Its behaviour depends heavily on the data describing its environment: available tools, prior observations, user intent, permissions and the current workflow state.
These become knobs for how autonomous, persistent and risk-tolerant the system should be. A useful agent has explicit stop conditions and budgets. It should not continue gathering data or taking actions indefinitely simply because more steps are possible.
Distillation can later compress stable agent behaviours such as query planning or result summarisation, but the permission and audit layers should remain explicit rather than being hidden inside model weights.
The knobs in detail
Each row is one adjustable property of the data asset, and what moving it tends to do.
| Knob | What you adjust | Likely effect |
|---|---|---|
| Available tools | What the agent may call | Bounds what it can possibly do |
| Permissions | Read-only vs. consequential actions | Separates research from execution |
| Memory | What state persists between steps | Controls coherence and leakage |
| Planning depth | How far ahead it reasons | Trades thoroughness against cost |
| Stop conditions | When the agent must halt | Prevents indefinite loops |
| Action budget | Maximum calls or spend per task | Caps blast radius and cost |
Applied: Stocks Assistant
A Stocks Assistant research agent could resolve a ticker, retrieve the latest filing and transcript, extract key metrics, compare them with prior periods and peer ranges, check market data, and assemble a cited briefing. A portfolio-review agent could do the same for each holding and aggregate concentration or catalyst risk. Each tool result should be stored with timestamp and provenance. Read-only research can be broadly automated, while consequential actions : placing a trade, modifying a paid subscription, sending a personalised alert : require explicit permissions and confirmation.
How to measure it
Evidence that the knob produced the intended behaviour, rather than shifting the problem elsewhere.
- Task completion and evidence coverage
- Unnecessary tool calls
- Failure recovery behaviour
- Policy compliance
- Replayable traces showing why each tool was chosen
Common mistakes
Automating consequential actions on the same footing as research.
Agents without replayable traces, which cannot be debugged or audited after the fact.