Agent Collaboration
The broad space: people and AI agents coordinate work, alone or in groups.
What is a Human-Agent Collaboration Client?
Human-Agent Collaboration is a human-centered form of Agent Collaboration. A Human-Agent Collaboration Client gives people one visible collaboration surface across one or more AI agents while keeping participants, context, approvals, durable conversation state, and local effects coherent.
Three terms, one collaboration picture.
Agent Collaboration is the broad space. Human-Agent Collaboration describes how people participate and retain authority; Multi-Agent Collaboration describes how several agents participate. Those two dimensions can overlap, and both belong to the broader Agent Collaboration category.
Human-Agent Collaboration
The human stays visible, understands the shared context, and remains the authority for approvals and effects.
Multi-Agent Collaboration
Several agents contribute different capabilities or viewpoints inside the same understandable experience.
More agents should create more capability, not more tabs to supervise.
You might ask one agent to shape an idea, another to research it, and a third to build or review the result. Coding agents make this easy to see, but the same problem appears in learning, writing, planning, and everyday knowledge work.
A collaboration client turns that scattered activity into one understandable experience: see who is working, give each participant the right context, approve meaningful effects at the moment they matter, and return to a durable conversation later.
People stay presentContext stays legibleApprovals stay intentional
A collaboration client owns the user-facing coordination boundary.
The exact implementation can vary, but the category centers on responsibilities that sit above model inference and task execution.
| Responsibility | What the client makes explicit |
|---|---|
| Participants | Which humans and agents are visible members of the conversation, and which agent is currently acting. |
| Conversation continuity | A durable user-facing history and identity that can span different provider-native sessions and runtimes. |
| Context disclosure | What history, files, tools, or protected content each admitted participant may receive. |
| Approval and effects | Which external disclosure or local effect is authorized, for what destination, scope, and purpose. |
| Heterogeneous adapters | How CLI, ACP, RPC, App Server, HTTP, SSE, and other agent interfaces fit into one client without losing useful native semantics. |
| Endpoint boundary | Which private keys, plaintext, runtime state, history, and trust decisions remain under endpoint control. |
| Evidence and status | What happened in an agent lifecycle and how that progress is presented consistently to the user. |
The collaboration client connects several layers into one human experience.
Models
Foundation models provide inference and reasoning. A collaboration client can work across agents that use different models and providers.
Agent runtimes
Agent runtimes execute work, manage tools, and maintain native sessions. The collaboration client gives those runtimes a shared human-facing context.
Orchestration
Orchestration decomposes, routes, sequences, and parallelizes work. It becomes one capability inside the broader collaboration experience.
Protocols
MCP, ACP, A2A, Lico Arc, HTTP, JSON-RPC, and other protocols provide interfaces and transport that the client can compose.
Claude Code, Codex, Cursor, Copilot, and OpenCode expose different surfaces.
LicoUp includes built-in conversation adapters for Claude Code, Codex, Cursor, GitHub Copilot, and OpenCode among thirteen agent targets. Their interfaces span stream JSON, App Server JSON-RPC, direct CLI, ACP, and loopback HTTP/SSE.
That diversity is the point: the collaboration layer can preserve the strengths of each agent interface while giving the human a coherent conversation and authority model above them.
Actual compatibility depends on the locally installed agent version, interface, and authentication setup. See current compatibility for the supported connection paths and their requirements.
Common distinctions.
How does Human-Agent Collaboration relate to Agent Collaboration?
Agent Collaboration is the broader category. Human-Agent Collaboration focuses on collaboration where the person remains a visible participant and authority, including multi-agent workflows that involve several AI agents.
How does a Human-Agent Collaboration Client relate to an agent orchestrator?
They compose naturally. Orchestration focuses on workflow execution; the collaboration client provides the durable human-facing conversation, participants, context, approvals, and authority around that work.
How does it relate to MCP or ACP?
Protocols and agent interfaces feed the client. The collaboration layer preserves their useful semantics while presenting one coherent user experience.
Does local-first mean offline-only?
Local-first keeps endpoint-controlled state as a primary authority while still allowing networked and federated collaboration.