Learning Modes of Adaptive Topology

How work units learn inside an Adaptive Topology.

What Are Learning Modes?

Learning Modes are lightweight, deliberate patterns that help individuals and teams continuously learn across boundaries—without reorganizing.

They are to learning what interaction modes (from Team Topologies) are to coordination: repeatable, intentional behaviors that make learning systemic.

In an Adaptive Topology, learning is not a side activity—it’s the primary flow. Learning Modes describe how that learning happens inside the structure.

How They Differ from Elevating Katas

• Elevating Katas are structured practices that help an organization move from a Resource or Delivery Topology toward an Adaptive Topology. They drive transformation—by shifting roles, rituals, responsibilities, or mindsets.

• Learning Modes describe how work units operate once they’re inside an Adaptive Topology, where continuous learning is part of the work itself.

Think of Elevating Katas as change agents, and Learning Modes as operating patterns for evolved teams.

Why Learning Modes Matter

Dynamic reteaming is often used to plug skill gaps. But this disrupts teams and slows progress.

Learning Modes offer a better alternative: practices that support multi-learning within long-lived teams, enabling them to evolve from within.

The Four Learning Modes

1. Scouting

2. Mirroring

3. Weaving

4. Synthesizing

Each mode enables learning across different dimensions: individuals, teams, roles, and systems.

1. Scouting

Explore the unknown. Sense early.

Small groups or individuals go outside their team to gather insight from other domains, users, or markets. Scouting brings early awareness of friction, trends, and ideas.

Example:

A backend developer joins customer support sessions to learn common complaints.

An AI agent might suggest, “Team X solved a similar problem—want a summary?”

2. Mirroring

Build empathy. Transfer tacit knowledge.

One person shadows another in a different role to understand their challenges, decision points, and context—not to replace them, but to see through their lens.

Example:

Developers sit with real users to observe how they work—learning what to improve or automate based on actual behavior and struggles.

3. Weaving

Learn across teams. Integrate perspectives.

Multiple teams work together—temporarily or regularly—on a shared challenge. This mode breaks silos and builds cross-team coherence.

Example:

Several teams collaborate to solve a major customer problem, combining technical, business, and support perspectives.

4. Synthesizing

Institutionalize the learning.

Insights from other modes are codified into shared tools, practices, or standards. This mode ensures learning sticks and scales.

Example:

After repeated Mirroring between QA and Dev, teams co-develop a shared onboarding template and update their Definition of Done.

What Learning Modes Enable

• Cross-role learning without reteaming

• Capability growth inside stable teams

• Resilience without overload

• Continuous adaptability in a fast-moving context

What Learning Modes Are Not

• Not Elevating Katas – which change the structure or culture to help reach an Adaptive state

• Not interaction modes – which focus on delivery coordination (like X-as-a-Service or Facilitation)

• Not team reshuffling – which breaks trust and momentum

How AI Accelerates Learning Modes

Learning has always required time, trust, and exposure. But with AI agents now embedded in everyday tools, learning can happen faster, just-in-time, and context-aware.

Here’s how AI augments each learning mode:

Scouting + AI

• Agents can proactively surface relevant examples, documents, or team artifacts.

• AI copilots can “scout” across internal tools, Slack, Confluence, or code to find prior solutions.

  → “This pattern was used in a similar case—want to see the code or talk to the team?”

Mirroring + AI

• AI agents can track sessions, summarize insights, or suggest questions to deepen learning during a mirroring experience.

  → “While observing the user workflow, notice how they hesitate on this step. Want to explore why?”

  
  

Weaving + AI

• During cross-team problem-solving, AI tools can surface common dependencies, conflicting priorities, or shared risks.

• Agents can document shared learnings across teams automatically.

  → “These 3 teams use different auth flows—would you like a synthesis report?”

  
  

Synthesizing + AI

• AI can generate first drafts of shared artifacts—onboarding guides, updated playbooks, or code documentation—based on conversation logs and patterns.

  → “Here’s a suggested update to the team’s Definition of Done based on what was discussed.”

Without AI, learning across teams is expensive and slow.

With AI, it becomes ambient and constant—multi-learning becomes a background capability.

Learning Modes don’t change. But AI makes them cheaper, faster, and easier to sustain and with manageable levels of cognitive load.

In Summary

In an Adaptive Topology, learning is the work.

They help teams stretch, connect, and grow—without needing to be rebuilt.

Used consistently, they reduce the need for structural change and make adaptability scalable.