sensemaking-concentrator

name	sensemaking-concentrator
description	Audit a multi-agent system for distributed sensemaking anti-patterns and recommend where to concentrate interpretation into a single agent, reducing conflicting signals and improving decision quality.

Sensemaking Concentrator Audit

Analyzes a multi-agent architecture for the distributed sensemaking anti-pattern -- where multiple agents independently interpret the same ambiguous signals and produce conflicting actions. Recommends where to concentrate interpretation into a single sensemaking agent that maintains context and resolves conflicts.

Trigger

Use when the user says "sensemaking audit", "agents are conflicting", "who interprets this signal", "concentrate sensemaking", "agent conflict resolution", "signal interpretation audit", or when debugging a multi-agent system where agents propose contradictory actions from the same input.

Phase 1: Intake

Accept the multi-agent system description. This can be:

• An agent manifest or architecture diagram (agent.yaml, CLAUDE.md, or verbal description)
• A specific incident where agents produced conflicting outputs
• A codebase path containing agent definitions
• A the agent platform-style multi-agent setup

If working with a codebase, use Glob and Read to find agent definitions, dispatch logic, and shared data sources.

Identify:

1. All agents in the system
2. Their declared responsibilities
3. Shared inputs (data sources, signals, events that multiple agents can see)

Phase 2: Map Signal Flow

For each shared input or signal:

1. List all agents that read it. If more than one agent reads the same signal and can act on it, flag it as a potential sensemaking conflict zone.
2. Classify each agent's role with that signal:
   • ROUTING -- agent passes the signal along without interpretation (automatable, low conflict risk)
   • SENSEMAKING -- agent interprets the signal, decides what it means, and acts on interpretation (high conflict risk when distributed)
   • ACCOUNTABILITY -- agent checks whether the signal was handled correctly (should remain distributed)
3. Identify conflict scenarios -- concrete examples where two agents could interpret the same signal differently and take contradictory actions.

Output a signal flow map:

Signal: [description]
  -> Agent A: SENSEMAKING (interprets as X, would do Y)
  -> Agent B: SENSEMAKING (interprets as X', would do Y')
  -> CONFLICT: Y and Y' are contradictory

Phase 3: Concentration Recommendations

For each conflict zone, recommend one of:

Option A: Designate a Sensemaking Owner

One agent becomes the sole interpreter for this signal class. Other agents receive the interpretation as a fact, not raw signal.

Option B: Add a Sensemaking Concentrator Agent

A new dedicated agent that receives all ambiguous signals, maintains cross-signal context, and emits resolved interpretations. Other agents subscribe to its outputs.

Option C: Conflict Resolution Protocol

Keep distributed interpretation but add an explicit resolution mechanism (voting, priority ranking, escalation to human) for when interpretations diverge.

For each recommendation, specify:

• Which agent should own sensemaking (and why)
• What interface the resolved interpretation should have
• What context the sensemaking agent needs to maintain
• What happens when the sensemaking agent is uncertain (escalation path)

Phase 4: Output

Present findings as:

# Sensemaking Concentration Audit

## System: [name]
**Agents:** [count]
**Shared signals:** [count]
**Conflict zones found:** [count]

## Signal Flow Map
[from Phase 2]

## Conflict Zones

### Zone 1: [signal description]
**Agents involved:** [list]
**Conflict type:** [interpretation divergence / action contradiction / priority conflict]
**Recommendation:** [A/B/C] -- [rationale]
**Implementation:** [specific changes]

## Summary
- Signals safely distributed (routing only): N
- Signals needing concentration: N
- Recommended new sensemaking agents: N
- Estimated conflict reduction: [qualitative assessment]

Verification

A good audit has:

• Every shared signal mapped to its reading agents
• Every multi-reader signal classified by role (routing/sensemaking/accountability)
• Concrete conflict scenarios, not abstract warnings
• Recommendations that are specific enough to implement (name the agent, describe the interface)
• No recommendation to centralize accountability (accountability should stay distributed)

Source

Extracted from Nate Kadlac newsletter (2026-04-12) -- management function decomposition (routing/sensemaking/accountability) applied to multi-agent system design. Based on the insight that distributing sensemaking causes the same failure mode in agent systems as in flat organizations.