Autonomous Swarm Intelligence at Cloud Scale
A self-organizing company of autonomous agents running on high-throughput cloud infrastructure.
SwarmAgents decomposes complex engineering and research objectives into parallel execution graphs, utilizing elastic cloud compute, persistent trajectory memory, and verified sandbox boundaries.
Engineered for Elastic AWS Cloud Workloads
SwarmAgents is architected from day one to harness the full breadth of Amazon Web Services, driving high-concurrency multi-agent orchestration, elastic model access, and persistent trajectory memory.
Amazon EKS & ECS
Containerized worker nodes spin up dynamically across AWS Spot & On-Demand compute, executing thousands of sandboxed agent sub-tasks concurrently.
Amazon Bedrock Routing
Zero-latency, private API routing across frontier foundation models (Claude, Llama) with support for AWS Trainium & Inferentia custom fine-tuned weights.
Amazon S3 Persistence
Durable, immutable artifact repository storing complete thought streams, diff trees, and execution traces with replay and audit capabilities.
VPC Isolation & KMS
Per-tenant namespace isolation, zero-trust container security policies, AWS KMS key management, and complete VPC private subnet support.
Same prompt · same model
What ChatGPT-style one-shot gets wrong — and SwarmAgents ships.
Not a different model. The framework: agents, verification gates, and repair. Measured on lfm2.5:8b-a1b-q4_K_M.
Prompt
Build a tiny static web UI. Create index.html (and optional app.js/styles.css if needed) such that: 1) The page title and a visible <h1> are exactly: SwarmAgents Counter 2) There is a button with id="inc" labeled Increment 3) There is an element with id="count" that starts at 0 and increases by 1 each click Use plain HTML/JS only (no frameworks). Do not modify package.json or the test file.
One-shot chat
Like ChatGPT / Gemini — same model, single reply
Failed objective gates (npm test). No working index.html.
package.jsontest/ui.test.js5.9s wall time
SwarmAgents
Same model · agents · gates · repair · world model
Passed gates. Shipped index.html + app.js that satisfy the contract.
index.htmlapp.jspackage.jsontest/ui.test.js323.7s wall time
+100pp lift on this fixture
Fair test: identical prompt and model. Chat arm is one-shot completion. Swarm arm uses the production leaf executor with verification gates. “ChatGPT / Google” here means that product shape — not their hosted APIs.
Why it ships
Built for budgeted swarms
The product is a time-aware decomposition engine with live control — not a chat wrapper.
Time-budget fit
Decomposition scales to your wall-clock. A 1-hour mission becomes a dense tree of 1–3 minute leaves — not a shallow checklist.
Auto-deepen for small models
Oversized nodes split until every leaf fits a local / small LLM worker. More micro-tasks, less hallucination.
Live steering
Pause, prefer, avoid, re-decompose, or inject leaves while the swarm runs — with applied vs queued honesty.
Role → model & quota
Route architect, coding, research, validation, and browser work to different providers with RPM and daily $ caps.
Sandbox visibility
Watch agents in shell or browser tiles under the neon DAG — tool calls and viewports as they work.
Rich executable leaves
Acceptance criteria, tools, dependencies, and deliverable paths on every leaf — not vague labels.
Describe an agent.
Get a model card.
Forge specialized LLM roles with prompt DNA and context specs. Plug them into workflows, decomposition trees, or let the orchestrator invoke them autonomously at runtime.
Create a new agent type
Describe what you want the agent to do — the system generates a prompt card, context specs, and tool bindings. It starts private in your library.
AWS Activate Startup Credits & Portfolio Tier FAQ
Detailed technical and organizational specifications answering core requirements for AWS Activate Startup Credits Portfolio Tier reviewers and institutional evaluators.
Direct Founder & Institutional Contact
Official custom domain contact: support@swarmagents.codes or team@swarmagents.codes
Why it feels different
Built for thousand-step work, not one-shot chat.
Decompose anything
Time-scaled trees that deepen until the work is leaf-sized — then keep going with parallel sub-agent workers.
Spawn a real team
Dynamic seats invent their own roles, own execution paths, and work concurrently across cloud nodes.
Share one workspace
Blackboard, heatmap, and shared context keep every agent synchronized without human babysitting.