Unified One-Screen Orchestration for Parallel Multi-Agent Development

1. The Hidden Cost of Parallel Agents: Scheduling Attention, Not Compute

When a single coding agent’s task chain becomes longer, developers naturally shift from serial command to parallel delegation: multiple agents explore different implementation paths, fix different defects, or refactor different modules at the same time. Compute and model capability are no longer the primary bottleneck. The bottleneck moves to the developer, who must act as the scheduler for multiple agent sessions.

The traditional approach is to open multiple terminal tabs or split the screen, place one session in each pane, and maintain a mental task ledger: which session is still running, which one is blocked waiting for human confirmation, and which one has already finished but has not been checked. The problem is not that the tools are too simple. The problem is that they lack three basic capabilities:

• No global view: Status is scattered across separate windows, making it impossible to see all in-flight tasks at once.
• No explicit human-intervention signal: Agents stop silently when they need input, and developers often discover the blockage minutes or even longer after it occurs.
• No low-cost path for context switching: Switching from session A to session B requires recalling where the conversation left off, which creates much higher cognitive overhead than machine scheduling overhead.

As a result, the industry’s demand for multi-agent orchestration has converged on a common capability: bringing scattered sessions into one unified interface that can be scanned, answered, and switched between foreground and background.

2. Core Abstraction: Treat Sessions as First-Class Entities

To achieve unified orchestration, the first step is to abstract a session from “just a terminal process” into a registrable, queryable, and migratable state entity. This provides three immediate benefits: sessions can be enumerated, serialized to disk, and remounted into a view at any time.

At minimum, a session should expose the following metadata. These fields determine the information density of the overview list:

Field	Purpose
Session ID and title	Quickly locate the task topic
Runtime state	Running / waiting for input / completed / failed / suspended
Whether human decision is required	The highest-priority routing signal
Summary of the latest reply	Allows progress assessment without entering the session
Last interaction time	Helps identify stuck sessions and recently completed sessions
Artifact clues	Indicates whether a branch, review request, or document has been produced

The state machine can be simplified as follows. The key point is to design “waiting for input” as a first-class state rather than hiding it inside logs:

       start            request human input          input completed
NEW ──────> RUNNING ─────────────> WAITING_INPUT ──────> RUNNING
                │                      │
                │ complete             │ timeout / cancel
                v                      v
            DONE / FAILED          SUSPENDED
                │
                └── backgrounding: convert into a mountable background session

A practical session state structure example:

{
  "session_id": "s-20260826-1030-a1",
  "title": "Refactor order state machine: migrate to explicit state enum",
  "state": "waiting_input",
  "needs_human": true,
  "last_message": "Detected two callers that depend on the old string states. Should a compatibility layer be kept?",
  "last_interaction_at": "2026-08-26T10:31:04Z",
  "workspace": "/worktrees/order-refactor",
  "artifacts": ["branch: refactor/order-state", "pr: #482"]
}

3. Overview View: Scan on One Screen and Reply In Place

The value of a unified view is that it turns “checking sessions one by one” into “scanning all sessions at once.” In terms of entry design, there are usually two parallel paths: one is to open a side overview from any session using arrow keys, and the other is to launch the view directly with an independent subcommand. Both paths share the same session registry, ensuring consistent state.

Interaction in the view should cover at least four layers, each reducing the cost of intervention:

1. List layer: One session per row, showing state indicators, whether human input is required, the latest reply summary, and the last interaction time. Sessions waiting for input must be visually distinguished, because they are the only category that requires immediate human action.
2. Preview layer: Selecting a session previews the latest conversation round, allowing the user to determine “what it is asking and whether the conclusion is usable” without switching context. This is a key design for reducing context-switching cost.
3. In-place reply layer: If a session is waiting for a decision, the user can enter a reply directly from the overview view. The session then continues execution without requiring navigation away.
4. Deep-dive layer: Pressing a confirmation key enters the full conversation history for scenarios that require long-form discussion or review of large outputs.

A lightweight sorting and filtering rule can further improve scanning efficiency. For example, sessions can be sorted by default as “waiting for input → recently active → idle,” and can be grouped by branch, working directory, or label:

PRIORITY = {"waiting_input": 0, "running": 1, "suspended": 2, "done": 3, "failed": 3}
def sort_sessions(sessions):
    # Sessions requiring human intervention come first.
    # Within the same group, sort by most recent interaction time in descending order.
    return sorted(sessions, key=lambda s: (PRIORITY[s.state], -s.last_interaction_ts))

4. Backgrounding and Foreground/Background Switching: Sessions Are No Longer Bound to Terminal Windows

One of the most overlooked engineering details in multi-agent orchestration is decoupling session lifecycle from the terminal window. Once sessions are serializable and recoverable, “foreground” and “background” become presentation differences at the view layer rather than execution differences.

Two common paths are:

• Move an existing session to the background: Execute a backgrounding command in the current session. The session continues running in the overview list, and the user can return to other work.
• Start directly in the background: Launch a session with a background-start parameter, creating it in one step and skipping foreground interaction. This is suitable for batch task assignment.

To make this mechanism reliable, the execution side needs to satisfy several conditions:

• Output redirection and ring buffering: Streaming output from background sessions should be written to log files or ring buffers to avoid memory growth during long-running tasks, while still allowing traceability.
• Recoverability: After a process crash or host restart, the system should be able to remount the workspace based on session metadata and resume execution.
• Heartbeat and health checks: The system should distinguish between “actually running” and “hung,” and issue alerts for sessions that produce no output beyond a threshold instead of remaining silent.
• Interrupt and cancel semantics: Graceful termination should be supported, temporary uncommitted changes should be rolled back, and half-finished states should be avoided.

5. Four Typical Practice Patterns

Pattern 1: Parallel exploration, converged review. Dispatch multiple agents for the same requirement, each implementing a different approach, such as two possible cache-layer redesigns. When they return, compare the results centrally and produce a set of branches and review requests. The benefit is that “choosing a solution” becomes parallel evidence gathering instead of serial trial and error.

Pattern 2: Long-running managed tasks. Examples include following up on review requests, syncing requirement boards, generating daily and weekly reports, and checking dependency upgrades. These tasks do not require a human to be present constantly, but the next scheduled run time must appear explicitly in the list. Otherwise, these tasks are easily forgotten.

Pattern 3: Context-isolated specialized agents. Split “implementers,” “test supplements,” and “document writers” into independent sessions, each maintaining its own dedicated context. This avoids goal drift and instruction contamination inside one overly long session.

Pattern 4: Result aggregation and exit checks. Each row’s state, combined with the session title and artifact clues in the preview, allows quick identification of sessions that have produced reviewable changes. One point must be clear: parallel output does not automatically reduce review cost. Humans remain the final gatekeepers. The role of the aggregation view is to make review scheduling more predictable, not to replace human judgment.

6. Four Engineering Priorities for Concurrency Governance

Execution isolation. Multiple agents modifying the same workspace will inevitably conflict. A common practice is to bind each session to an independent working copy and an independent branch, such as creating isolated directories through a worktree mechanism, then converging through branch merges.

# Each session gets an independent working copy to avoid file-level conflicts
git worktree add ../wt-a1 -b agent/explore-cache-a
git worktree add ../wt-a2 -b agent/explore-cache-b

Quota and rate governance. The number of concurrent sessions, model invocation frequency, and context length all amplify cost and rate-limiting risk. It is advisable to set a global concurrency limit, a per-session budget limit, and queuing or degradation strategies, such as suspending requests instead of failing immediately when quota is exceeded.

Security and least privilege. Agents need to execute commands, read and write files, and access credentials, which is inherently high-risk. Least privilege should be followed: grant read/write access only to directories required by the task, issue credentials per session for short periods, require human confirmation for destructive operations, and apply network egress whitelisting as needed.

Observability. Session event streams should be connected to unified logging and metrics channels, covering at least the number of sessions, waiting-for-input duration, failure rate, average task duration, and consumption per task. The distribution of waiting-for-input duration is the most valuable metric, because it directly quantifies the degree to which humans have become the bottleneck.

7. Common Cloud Capability Combinations for Deployment

If a multi-agent orchestration platform is deployed on Baidu AI Cloud, the typical architecture is as follows: a container platform hosts session runtimes and guarantees isolation, object storage stores session records and artifact snapshots, a message queue handles task distribution and completion notifications, logging services and monitoring alerts observe runtime state, and a gateway centrally handles authentication and quota control. On Baidu AI Cloud, these correspond to general-purpose capabilities in containers, storage, messaging, logging, and monitoring, and can be reused through an existing cloud-native architecture. No multi-agent-specific component is required.

8. Conclusion and Getting-Started Recommendations

The efficiency gain in parallel multi-agent development does not come from “faster models.” It comes from redesigning the scheduling interface: turning sessions into first-class state entities, using a unified overview to eliminate scattered windows, using an explicit waiting-for-input state to surface human intervention earlier, using foreground/background switching to decouple execution from terminals, and using isolation and quota governance to protect cost and security boundaries.

For implementation, a gradual path is recommended: first, create unified state records and a read-only overview for a group of sessions, and verify whether “scan to know” solves the problem of slow blockage detection. Then add backgrounding and in-place replies to compress context-switching cost. Finally, integrate isolation, quota governance, and observability to turn occasional practices into reusable engineering processes.