abc-agent-management

name	abc-agent-management
description	Functional-analysis protocol for iteratively improving subagent performance. Adapted from Antecedent-Behavior-Consequence (ABC) analysis in applied behavior analysis. Use when delegating to a subagent, when an agent returns a poor result, when a pattern of similar failures appears across runs, or when coaching a prompt toward reliability.

ABC Agent Management

Treat subagent failures as behaviors with antecedents (the prompt/context that produced them) and consequences (the feedback loop that sustains them), not as traits of the agent. Fix the environment, not the "agent."

Mapping

ABA construct	Agent equivalent
Antecedent (immediate)	The brief given to the agent, constraints, exemplars
Setting event (distal)	System prompt, prior turns, tool set, context-window state
Behavior (observable)	Tool calls, output, artifacts, the scope the agent chose
Consequence	Your response: accept, correct, retry, reject, downstream effect
Function	What "need" the behavior met (escape, completeness theater, pattern-match, reinforcement history)
Reinforcement	Anything that makes the behavior more likely next time
Extinction	Removing what maintained the behavior; expect a burst before improvement

Protocol

1. Operationally define the behavior. No trait words ("sloppy", "lazy"). State what was observed, measurable. "Emitted 200-line plan, 0 tool calls, on an implementation task" — not "didn't do the work."

2. Record A, B, C before intervening. Skipping this is how you fix the wrong thing.

   • A: Exact brief. Tool set. What was in the agent's window?
   • B: Exact observed behavior. Counts where possible (tool calls, files touched, lines emitted).
   • C: What happened next. Accept, correct inline, silent retry? A silent accept reinforces the shape.

3. Hypothesize function. Why was this the path of least resistance given A and C? Recurring agent functions:

   • Escape/avoidance: ambiguous task → deflect to planning, research, or questions.
   • Completeness theater: unclear done-state → over-scope, exhaustive output.
   • Pattern-match: exemplar in context (even ill-fitting) → produce its shape.
   • Reinforcement history: prior run was accepted → repeat the shape regardless of fit.

4. Prefer antecedent changes over consequence changes. Tightening the setup almost always beats tuning the reaction. See Antecedent Levers below.

5. Differential reinforcement: name the replacement. Never only suppress. "Instead of producing a plan, make the first edit" beats "don't over-plan."

6. Change one variable at a time. Single-subject design. Edit the brief, the tool set, and the exemplar simultaneously and you learn nothing when it works.

7. Expect an extinction burst. Removing a condition that was reinforcing a behavior can make the first retry look worse, not better. Do not roll back on one bad run.

8. Log the iteration. Each ABC cycle: antecedent changed, behavior observed, whether it moved. Patterns that recur across agents belong in bd remember so future sessions inherit the lesson.

Antecedent Levers

Concrete edits to the brief/context, in rough order of effect:

• Done-state. Name the exact artifact or check that means "done." Removes completeness theater.
• Scope fence. State what is not in scope. Removes scope creep.
• Exemplar. Provide one example of the desired output shape. Agents pattern-match; supply the right pattern.
• Tool constraint. Provide only the tools needed. Extra tools invite detours.
• Context prune. Remove irrelevant context. A long plan already in the window predicts more planning.
• Failure hypothesis. State the specific mistake to avoid, with the reason. "Do not open a PR; we want the diff reviewed first because X."
• Initiation cue. Name the first observable action. "Start by running the failing test." Removes where-to-begin ambiguity.

Common Pitfalls

• Trait framing. "This agent is bad at X." Behavior is a function of environment; the same agent behaves differently under different antecedents.
• Imprecise behavior definition. You will fix whatever imprecise thing you wrote down. Write it precisely.
• Stacked changes. Five prompt edits at once leaves no signal.
• Silent accept. Merging a mediocre result without comment reinforces the shape. Either correct it or state the acceptance criteria met.
• Consequence obsession. Hours tuning retry feedback usually lose to five minutes tightening the antecedent.
• Conflating setting events with immediate antecedents. A bloated context window (setting event) and the specific brief line (immediate antecedent) are both editable, but the levers differ. If every agent run fails, suspect the setting event; if only this run failed, the brief.

When Not To Use

• Single-turn, low-stakes agent calls where iteration is not planned.
• Research subagents where the output is the artifact and there is no retry.

Source

Adapted from ABC analysis / functional behavior assessment. Reference: Indiana Resource Center for Autism (Indiana University Bloomington), Observing Behavior Using A-B-C Data: https://iidc.indiana.edu/irca/articles/observing-behavior-using-a-b-c-data.html Underlying framework: applied behavior analysis (Cooper, Heron, Heward, Applied Behavior Analysis, 3rd ed.).