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Mission Control

Mission Control is Panopticon’s default landing view — a unified monitoring interface for all active projects, features, and AI agent activity. It includes Shadow Engineering, a companion mode for teams adopting AI-assisted development incrementally.

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Layout

Mission Control uses a two-panel layout:
  • Sidebar (left): Resizable project tree showing all configured projects with their active features/workspaces
  • Main panel (right): Feature activity view, planning artifacts, and status reviews

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Project Tree

Projects appear as collapsible folders. Each active feature shows:
ElementDescription
Status indicatorSpinner if agent is running, circle if idle
Issue IDPAN-123, MIN-456, etc.
State labelIdle, Planning, In Progress, In Review, Done, Has Context, Suspended
State labels are computed from multiple signals: tmux session presence, heartbeat freshness (10-minute threshold), review/test status from the central review-status.json, and agent state files.

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Activity View

When you click a feature, the main panel shows all agent sessions unified into one scrollable view. Each session appears as a collapsible section with:
  • Type badge: PLANNING (blue), WORK (green), REVIEW (amber), TEST (indigo), MERGE (pink)
  • Model: Which Claude/LLM model the agent used (e.g., Opus 4.6, Sonnet 4.5)
  • Duration: How long the session ran
  • Status dot: Green pulse (running), gray (completed), red (failed)
  • Transcript: Full agent output with tail-anchored auto-scroll for running sessions

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Section Isolation Mode

Click a section header to view it full-screen. Press Esc or use the browser back button to return. The isolated view includes model display and a prominent keyboard hint.

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Badge Bar

Quick-access badges appear below the feature header:
BadgeWhat it shows
TasksOpens beads task panel for the feature
STATERenders STATE.md from the workspace’s .planning/ directory
PRDRenders PRD.md (grayed out if not generated)
StatusAI-generated progress review comparing code against PRD
InferenceShadow Engineering inference document (only for shadow workspaces)
DiscussionsSynced issue tracker discussions
TranscriptsUploaded meeting transcripts
UploadAttach transcripts or notes to the feature
SyncPull latest discussions from GitHub/Linear

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Status Reviews

Click the Status badge to generate an AI-powered progress review. The review:
  1. Gathers context: PRD, STATE.md, git diff, commit log, file changes, review/test status
  2. Sends everything to the configured LLM (set via Settings > Workflow Agents > Planning)
  3. Returns a structured analysis: summary, PRD coverage table, risk assessment, and recommendations
If no API key is configured, a static template with raw data is shown as fallback. Any configured provider (Anthropic, OpenAI, Google, Kimi, Z.AI) can be used for status reviews.

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Shadow Engineering

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Concept

Shadow Engineering is for teams that want AI assistance without replacing their existing workflow. Instead of AI doing the work, AI observes, documents, and assists.
Standard ModeShadow Engineering
Planning agent creates PRDMonitoring agent infers plan from artifacts
Work agent implements featuresObserver watches human PRs and comments
AI drives the workAI shadows the work
PRD.md generatedInference Document produced

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How It Works

  1. Enable Shadow Mode: When creating a feature workspace, toggle “Shadow Engineering” on
  2. Monitoring Agent: Analyzes available artifacts (issue description, tracker comments, meeting transcripts, PR descriptions) and produces an Inference Document — the AI’s working understanding of what the team is building
  3. Inference Document: Not a prescriptive PRD but an inferred understanding. Surfaces gaps, ambiguities, and risks. Updates as new artifacts arrive
  4. Observer Agent: Watches the team’s actual development. Comments on PRs with observations and suggestions. Only commits code when explicitly asked

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Value Proposition

AI that learns your team before it leads.
Shadow Engineering lets existing engineers keep working their way while Panopticon’s AI observes, documents, and assists. It learns the codebase, patterns, and team approach — so when you’re ready to go further, the AI already understands how you build. Ideal for:
  • Teams wanting to adopt AI-assisted development gradually
  • Organizations that need to maintain existing workflows during transition
  • Leaders who want AI oversight and documentation without AI-driven changes
  • Teams evaluating AI coding tools who need a low-risk entry point

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Shadow Workspaces in the UI

Shadow workspaces are visually distinct in Mission Control:
  • Marked with a shadow indicator in the project tree
  • Show the Inference badge instead of generating PRDs
  • Inference modal includes an explanation of what the document represents

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Planning Artifacts

Each feature workspace has a .planning/ directory: 
feature-pan-XXX/.planning/
├── PRD.md                    # Product requirements (generated or manual)
├── STATE.md                  # Current progress notes
├── INFERENCE.md              # Shadow Engineering inference document
├── STATUS_REVIEW.md          # Latest AI-generated status review
├── transcripts/              # Uploaded meeting transcripts
├── discussions/              # Synced tracker discussions
└── notes/                    # Ad-hoc notes and documents

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Uploading Artifacts

Use the Upload badge to attach markdown or text files. Files are categorized as transcripts or notes and stored in the appropriate subdirectory.

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Syncing Discussions

Click Sync to pull the latest comments from GitHub Issues or Linear. Discussions are converted to markdown and stored in .planning/discussions/.

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Configuration

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Adding Projects

Projects are configured in ~/.panopticon/config.yaml. Any project with active workspaces appears in Mission Control’s project tree.

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Model Selection

The model used for status reviews is configured in Settings > Workflow Agents > Planning. See Configuration for details.