hosted-agents

Hosted Agent Infrastructure

Hosted agents run in remote sandboxed environments rather than on local machines. When designed well, they provide unlimited concurrency, consistent execution environments, and multiplayer collaboration. The critical insight is that session speed should be limited only by model provider time-to-first-token, with all infrastructure setup completed before the user starts their session.

When to Activate

Activate this skill when:

• Building background coding agents that run independently of user devices
• Designing sandboxed execution environments for agent workloads
• Implementing multiplayer agent sessions with shared state
• Creating multi-client agent interfaces (Slack, Web, Chrome extensions)
• Scaling agent infrastructure beyond local machine constraints
• Building systems where agents spawn sub-agents for parallel work

Do not activate this skill for adjacent work owned by other skills:

• Designing the autonomous research loop, novelty gates, rollback policy, or merge boundaries: harness-engineering.
• Choosing supervisor, swarm, or handoff topology without hosted infrastructure concerns: multi-agent-patterns.
• Designing the tools used by a hosted agent, such as spawn/status tools or PR tools: tool-design.
• Managing file-backed state inside a session rather than the hosted runtime itself: filesystem-context.

Core Concepts

Move agent execution to remote sandboxed environments to eliminate the fundamental limits of local execution: resource contention, environment inconsistency, and single-user constraints. Remote sandboxes unlock unlimited concurrency, reproducible environments, and collaborative workflows because each session gets its own isolated compute with a known-good environment image.

Design the architecture in three layers because each layer scales independently. Build sandbox infrastructure for isolated execution, an API layer for state management and client coordination, and client interfaces for user interaction across platforms. Keep these layers cleanly separated so sandbox changes do not ripple into clients.

Detailed Topics

Sandbox Infrastructure

The Core Challenge Eliminate sandbox spin-up latency because users perceive anything over a few seconds as broken. Development environments require cloning repositories, installing dependencies, and running build steps -- do all of this before the user ever submits a prompt.

Image Registry Pattern Pre-build environment images on a regular cadence (every 30 minutes works well) because this makes synchronization with the latest code a fast delta rather than a full clone. Include in each image:

• Cloned repository at a known commit
• All runtime dependencies installed
• Initial setup and build commands completed
• Cached files from running app and test suite once

When starting a session, spin up a sandbox from the most recent image. The repository is at most 30 minutes out of date, making the remaining git sync fast.

Snapshot and Restore Take filesystem snapshots at key points to enable instant restoration for follow-up prompts without re-running setup:

• After initial image build (base snapshot)
• When agent finishes making changes (session snapshot)
• Before sandbox exit for potential follow-up

Git Configuration for Background Agents Configure git identity explicitly in every sandbox because background agents are not tied to a specific user during image builds:

• Generate GitHub app installation tokens for repository access during clone
• Set git config user.name and user.email when committing and pushing changes
• Use the prompting user's identity for commits, not the app identity

Warm Pool Strategy Maintain a pool of pre-warmed sandboxes for high-volume repositories because cold starts are the primary source of user frustration:

• Keep sandboxes ready before users start sessions
• Expire and recreate pool entries as new image builds complete
• Start warming a sandbox as soon as a user begins typing (predictive warm-up)

Agent Framework Selection

Server-First Architecture Structure the agent framework as a server first, with TUI and desktop apps as thin clients, because this prevents duplicating agent logic across surfaces:

• Multiple custom clients share one agent backend
• Consistent behavior across all interaction surfaces
• Plugin systems extend functionality without client changes
• Event-driven architectures deliver real-time updates to any connected client

Code as Source of Truth Select frameworks where the agent can read its own source code to understand behavior. Prioritize this because having code as source of truth prevents the agent from hallucinating about its own capabilities -- an underrated failure mode in AI development.

Plugin System Requirements Require a plugin system that supports runtime interception because this enables safety controls and observability without modifying core agent logic:

• Listen to tool execution events (e.g., tool.execute.before)
• Block or modify tool calls conditionally
• Inject context or state at runtime

Speed Optimizations

Predictive Warm-Up Start warming the sandbox as soon as a user begins typing their prompt, not when they submit it, because the typing interval (5-30 seconds) is enough to complete most setup:

• Clone latest changes in parallel with user typing
• Run initial setup before user hits enter
• For fast spin-up, sandbox can be ready before user finishes typing

Parallel File Reading Allow the agent to start reading files immediately even if sync from latest base branch is not complete, because in large repositories incoming prompts rarely touch recently-changed files:

• Agent can research immediately without waiting for git sync
• Block file edits (not reads) until synchronization completes
• This separation is safe because read-time data staleness of 30 minutes rarely matters for research

Maximize Build-Time Work Move everything possible to the image build step because build-time duration is invisible to users:

• Full dependency installation
• Database schema setup
• Initial app and test suite runs (populates caches)

Self-Spawning Agents

Agent-Spawned Sessions Build tools that allow agents to spawn new sessions because frontier models are capable of decomposing work and coordinating sub-tasks:

• Research tasks across different repositories
• Parallel subtask execution for large changes
• Multiple smaller PRs from one major task

Expose three primitives: start a new session with specified parameters, read status of any session (check-in capability), and continue main work while sub-sessions run in parallel.

Prompt Engineering for Self-Spawning Engineer prompts that guide when agents should spawn sub-sessions rather than doing work inline:

• Research tasks that require cross-repository exploration
• Breaking monolithic changes into smaller PRs
• Parallel exploration of different approaches

API Layer

Per-Session State Isolation Isolate state per session (SQLite per session works well) because cross-session interference is a subtle and hard-to-debug failure mode:

• Dedicated database per session
• No session can impact another's performance
• Architecture handles hundreds of concurrent sessions

Real-Time Streaming Stream all agent work in real-time because high-frequency feedback is critical for user trust:

• Token streaming from model providers
• Tool execution status updates
• File change notifications

Use WebSocket connections with hibernation APIs to reduce compute costs during idle periods while maintaining open connections.

Synchronization Across Clients Build a single state system that synchronizes across all clients (chat interfaces, Slack bots, Chrome extensions, web interfaces, VS Code instances) because users switch surfaces frequently and expect continuity. All changes sync to the session state, enabling seamless client switching.

Multiplayer Support

Why Multiplayer Matters Design for multiplayer from day one because it is nearly free to add with proper synchronization architecture, and it unlocks high-value workflows:

• Teaching non-engineers to use AI effectively
• Live QA sessions with multiple team members
• Real-time PR review with immediate changes
• Collaborative debugging sessions

Implementation Requirements Build the data model so sessions are not tied to single authors because multiplayer fails silently if authorship is hardcoded:

• Pass authorship info to each prompt
• Attribute code changes to the prompting user
• Share session links for instant collaboration

Authentication and Authorization

User-Based Commits Use GitHub authentication to open PRs on behalf of the user (not the app) because this preserves the audit trail and prevents users from approving their own AI-generated changes:

• Obtain user tokens for PR creation
• PRs appear as authored by the human, not the bot

Sandbox-to-API Flow Follow this sequence because it keeps sandbox permissions minimal while letting the API handle sensitive operations:

1. Sandbox pushes changes (updating git user config)
2. Sandbox sends event to API with branch name and session ID
3. API uses user's GitHub token to create PR
4. GitHub webhooks notify API of PR events

Client Implementations

Slack Integration Prioritize Slack as the first distribution channel for internal adoption because it creates a virality loop as team members see others using it:

• No syntax required, natural chat interface
• Build a classifier (fast model with repo descriptions) to determine which repository to work in
• Include hints for common repositories; allow "unknown" for ambiguous cases

Web Interface Build a web interface with these features because it serves as the primary power-user surface:

• Real-time streaming of agent work on desktop and mobile
• Hosted VS Code instance running inside sandbox
• Streamed desktop view for visual verification

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