Multi-agent UIs collapse all agents into "the AI", making it impossible for the user to attribute actions, outputs, or reasoning to a specific agent.
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Agent2Agent (A2A) Protocol
Agent Workflow
EmergingAgent Identity
Distinct Faces for Distinct Agents
When supervisor-worker setups dispatch many specialist agents, every agent action, output, and reasoning step needs a distinct visual identity so the user can tell them apart at a glance. Avatar, color, and role label — used consistently across logs, dashboards, and outputs.
Metaphor
Crew Roster
Interaction
Visual + Named Distinction
Use case
Multi-Agent Supervision, Specialist Pipelines, Audit Trails
Failure mode
Voice Collapse
Control mechanisms
Avatars · Color Codes · Role Labels · Action Attribution
Reference examples
Superhuman Go agent roster · CrewAI Agent Cards · Claude Code subagents · Manus AI internal agent roles · AutoGen Studio agent gallery
Interactive Example
The live side-by-side is the comparison.
This prototype already contains the contrast between chat and the pattern response. It stands as the primary comparison surface for this page.
Many agents, one anonymous voice.
Supervisor-worker setups dispatch a dozen specialists, but the interface collapses them all into 'the AI.' When an action lands or a reasoning step looks off, the user cannot attribute it to any specific agent — accountability dissolves into a single undifferentiated stream, and you debug a crew you can't tell apart.
Agent Identity gives each agent a stable face — avatar, color, role label — rendered consistently across logs, dashboards, and outputs. Every action carries the visual fingerprint of who produced it, so the user can address, redirect, or pause a single agent by name. Attribution becomes a glance instead of a metadata hunt.
You cannot supervise a team whose members you can't tell apart — identity is the precondition for accountability.
DOPE Lens
How this pattern improves AI interaction quality.
Observability
Every action and output carries a stable visual identifier of the agent that produced it
Directability
Users can address, redirect, or pause individual agents by name
Predictability
The same agent always renders with the same identity across surfaces
Explainability
Each agent identity carries a role description and capability boundary
Fit
Where it earns its place.
Use it
when
when
Multiple agents act on the user behalf and the user needs to attribute actions and outputs to specific agents at a glance.
Avoid
when
when
The system runs a single agent and identity adds visual chrome without information.
In the wild
Where this pattern already ships.
The pattern is shipping but still maturing across products. Entries below show how different teams have approached it; expect variation as the space settles.
Superhuman Go · agent roster
Superhuman
The consumer case the other four entries lack — they are all developer tools. Go is a designed character with motion built into its UI as a deliberate signal that it is thinking, coordinating a roster of named specialists the user curates: Grammarly, Email Assistant, Knowledge Checker, Daily Brief, plus custom agents from a store. The identity work is real but asymmetric — the coordinator has a face, while the specialists carry names and remits without a per-agent colour following their output across surfaces.
Visit Superhuman ↗
CrewAI · Agent Cards
CrewAI
Every agent gets a Name, Role, Goal, and Backstory rendered as a card in the playground. Outputs are tagged with the agent that produced them. The clearest production framing of distinct-agents-as-distinct-faces.
Visit CrewAI ↗
Claude Code · subagents
Anthropic
Specialist agents (code-reviewer, debugger, frontend-developer, etc.) defined by name + role + tool allowlist, plus a `color` frontmatter field that renders each subagent in its own display color in the task list and transcript. Every dispatched call surfaces the subagent name, so attribution survives across long sessions — the clearest shipping proof of color-coded agent identity.
Visit Anthropic ↗
Manus AI · internal agent roles
Monica · Manus
Internal Planner / Executor / Verifier architecture — distinct roles exist in the orchestration layer, but they are not consistently exposed as labelled agents in the user-facing live log. The gap between internal role separation and visible identity is exactly what this pattern names.
Visit Monica · Manus ↗
AutoGen Studio · agent gallery
Microsoft Research
Multi-agent orchestration with a gallery view: each agent rendered as a card with system prompt, tools, and history. Conversations between agents are visualised as labelled message flows.
Visit Microsoft Research ↗
Research Frame
Why this pattern exists, and where it breaks.
Give each agent a stable visual and named identity — avatar, color, role label — rendered consistently across every surface where its work appears.
Chat shows one assistant voice; multi-agent reality has many — and the user needs to tell them apart at a glance, not by reading metadata.
Identity bloat (too many similar avatars) defeats the purpose; superficial naming can imply more specialization than actually exists.
Common questions
What people ask about this pattern.
When should I use the Agent Identity pattern?
+
Multiple agents act on the user behalf and the user needs to attribute actions and outputs to specific agents at a glance.
When should I avoid the Agent Identity pattern?
+
The system runs a single agent and identity adds visual chrome without information.
What problem does Agent Identity solve?
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Multi-agent UIs collapse all agents into "the AI", making it impossible for the user to attribute actions, outputs, or reasoning to a specific agent.
Why is chat the wrong fit for this?
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Chat shows one assistant voice; multi-agent reality has many — and the user needs to tell them apart at a glance, not by reading metadata.
Grounded in Research
Not just opinion.
The papers, guidelines, and usability studies this pattern rests on.
Google, now Agentic AI Foundation (Linux Foundation)
Industry standard · 2025
Open spec for inter-agent communication with optionally signed Agent Cards (JWS). It signs who an agent is, not what it delivered; task-outcome attestations are still open community proposals.
Related Patterns
Curated connections, not just neighbors.
Hand-picked relationships — what this pattern pairs with in practice, what it replaces, what it builds on, and what builds on it.
Often paired with
Supervisor-Worker
Supervisor-Worker splits a task across specialist agents; Agent Identity shows at a glance which agent did which part.
Often paired with
Action Audit & Undo
Action Audit & Undo logs every agent action; Agent Identity shows which agent took each one, not a generic AI label.
Often paired with
Agentic Dashboard
Agentic Dashboard gives each agent its own lane; Agent Identity keeps that agent recognizable in logs and outputs too.