agent-swarm

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Agent Swarm is your Company's Compounding Intelligence Layer. A system of AI agents that remember, reason, act and get better with every task.

AI-Native · Compounds · Presence · Harness & LLM-Agnostic · Your Infra · Your Memory ·

What it does

Agent Swarm runs a team of AI agents that coordinate autonomously. A lead agent receives tasks (from Slack, GitHub, GitLab, Linear, Jira, email, or the API), breaks them down, and delegates to worker agents running in isolated environments (Docker). Workers execute tasks, ship solutions, and write their learnings back to a shared memory so the whole swarm gets smarter every session.

You can run agents for Marketing, Product, UX, Engineering, Support, Operations, HR, Finance, or any role you can think of. A centralized Lead coordinates them, and they share the learnings horizontally. That's the true difference between AI First and AI Native.

Agent Swarm is the shared cloud brain and muscle that makes your whole company better every day.

Sometimes humans are the blocker. We can help you. Contact us contact@desplega.sh.

Learn more in the architecture overview.

flowchart LR
    subgraph IN["Tasks come in"]
        direction TB
        S["Slack"]
        G["GitHub / GitLab"]
        E["Email"]
        A["API / CLI"]
    end

    LEAD(["Lead Agent<br/>plans &amp; delegates"])

    subgraph WORKERS["Workers in Docker"]
        direction TB
        W1["Worker"]
        W2["Worker"]
        W3["Worker"]
    end

    subgraph BRAIN["Persistent brain"]
        direction TB
        MEM["Memory<br/>(vector search)"]
        ID["Identity<br/>(SOUL, CLAUDE.md)"]
    end

    subgraph OUT["Work ships"]
        direction TB
        PR["Pull Requests"]
        REPLY["Slack replies"]
        EMAIL["Email replies"]
    end

    IN --> LEAD --> WORKERS
    WORKERS -->|reads context| BRAIN
    WORKERS -->|writes learnings| BRAIN
    WORKERS --> OUT

Known Use Cases

Use cases that are used daily by ourselves and others. Each playbook contains: the agents, the tools & skills, and workflows & schedules behind it. Browse all playbooks →

• Feature Development — Integrated with Linear and GitHub to take feature requests from Slack and turn them into pull requests.
• Lead Prospecting — Integrate your prospecting tools with the swarm and let agents handle outreach, scheduling, and follow-up.
• Content Generation — Generate engagement tools, blog posts, manage social media presence, update your website, and more.
• UX Command Center — Agents that keep your product usable: record agentic sessions, enforce your design system, and mine user logs to detect and propose UX improvements.
• Proactive Customer Support — Agents that oversee your top accounts, prepare scheduled reports, and leverage everything they know about your platform to keep those accounts up to date.
• Code Health & Alert Management — Datadog, New Relic, Sentry, or any alerting tool can kick off fixes or new proposals. Monitor code health and propose improvements weekly, daily, or hourly.
• Reports from Multiple Sources — Integrate your data warehouse to generate tailored reports and answer the key questions your team has, with fresh data. Your BI tool may be a thing of the past.
• Self-Documenting & Release Reports — Update your docs and use frameworks like Remotion, qa-use, and browser-use to generate release videos and rich documentation in seconds, at the cadence you need.
• Do you have a cool playbook to share? Send us a PR!

The patterns that compound. Five recipes show up in nearly every playbook — they're how the swarm stays reliable as it scales: Litmus Tests (LLM-as-judge quality gates) · Drain Loops (one ticket → a chain of reviewable PRs) · HITL Gates (pause for human approval on irreversible steps) · Per-Customer Working Directories (context that compounds per account) · No-op Workflows (skip silently when nothing changed). See all patterns →

Check our templates for a quick start.

Highlights

• Lead/worker orchestration in Docker — isolated dev environments, priority queues, pause/resume across deploys. Architecture →
• Compounding memory & persistent identity — agents remember past sessions and evolve their own persona, expertise, and notes. Memory → · Agents →
• Multi-channel inputs — Slack, GitHub, GitLab, email, WhatsApp, Linear, Jira, and the HTTP API all create tasks. Integrations
• Workflow engine with Human-in-the-Loop — DAG-based automation with approval gates, retries, and structured I/O. Workflows →
• Scheduled & recurring tasks — cron-based automation for standing work. Scheduling →
• Durable script workflows — launch background script runs, inspect their journals, and track them from the dashboard when a one-shot script-run is too small. Guide →
• E2B-backed eval harness — run a scenario × harness-config matrix against real swarm stacks, capture transcripts/artifacts, and grade outcomes with deterministic checks plus LLM or agentic judges. Guide →
• Harness & LLM agnostic — run with Claude Code, Claude Bridge, OpenAI Codex, pi-mono (Anthropic, OpenRouter, or Amazon Bedrock), Devin, Claude Managed Agents, raw LLMs, or opencode. Tasks, schedules, and workflow agent-task nodes can use portable modelTier intent (smol, regular, smart, ultra) and resolve it per worker/provider at run time. Harness config → · Add a new provider →
• Follow-up continuity across all harnesses — child tasks inherit a bounded prior-task context preamble built from the task chain, so continuity survives restarts and works the same across every p