# One agent is a feature. A fleet is a company.

> Multi-agent systems that plan, split the work, run in parallel and review each other. We design the orchestration, state, permissions and evals, and run our own studio on it.

URL: https://ai.arcvs.tech/en/services/multi-agent-orchestration · Português: https://ai.arcvs.tech/pt/servicos/orquestracao-de-agentes.md · ARCVS AI: https://ai.arcvs.tech/en.md

Orchestrated agents that plan, split the work, run in parallel, check each other and hand back clean results. We run our own delivery on this pattern every day, so we build yours from experience, not from a blog post.

- **Week 1**: Prototype on your data
- **Day 30**: Production pilot
- **100%**: Yours: code & weights

## Single agents hit a wall. Orchestration breaks it.

Long tasks blow the context window, parallel agents step on each other, and nobody knows which agent changed what. Orchestration fixes that: one manager owns state and decisions, workers run isolated, reviewers resolve conflicts, and every step is traceable.

## What we build

- **Delivery loops**: A manager agent cuts a backlog into groups, parallel executors each ship one group on its own branch, a reviewer resolves conflicts. It's how we run ARCVS.
- **Research & analysis pipelines**: Fan out to dozens of sources, verify claims adversarially, merge into one sourced report.
- **Document-heavy operations**: Intake, classification, extraction, validation and filing as separate agents with hand-offs you can audit.
- **Agent ↔ human boards**: Agents work a queue, humans approve at the gates. The board stays the source of truth.

## What you get

- Orchestration architecture: roles, hand-offs, state ownership
- Isolation: sandboxes, worktrees, scoped credentials per agent
- Conflict and failure handling, retries, budgets per run
- MCP servers to expose your systems to agents safely
- Run dashboard: who did what, cost, duration, outcome

## Stack

Claude Agent SDK, LangGraph, MCP, Temporal, Vercel Workflow, Queues, Postgres, Python, TypeScript

## FAQ

### When do we need multiple agents instead of one?

When the task is too long for one context, when parts can run in parallel, or when you want independent review. If one agent does the job, we'll tell you and build one.

### Do you use a framework or build from scratch?

Whatever fits. LangGraph, the Claude Agent SDK, durable workflow engines or plain code. The orchestration logic stays readable and in your repo.

### How do you keep parallel agents from conflicting?

Isolated workspaces per agent, a single writer for shared state, and a reviewer role that only activates on real conflicts.

### Is this real or theoretical?

Real. Our own delivery loop runs a manager, parallel executors and a conflict reviewer that open and merge pull requests.

## Other capabilities

- [AI agents](https://ai.arcvs.tech/en/services/ai-agents): Agents that use your tools, follow your rules and finish real tasks end to end.
- [Model training & fine-tuning](https://ai.arcvs.tech/en/services/llm-fine-tuning): Fine-tuned and distilled models that win on your task, run cheaper, and belong to you.
- [RAG & knowledge systems](https://ai.arcvs.tech/en/services/rag-knowledge-systems): Search and answers over your company data, with citations, permissions and measured accuracy.
- [AI automation](https://ai.arcvs.tech/en/services/ai-automation): Workflows where AI reads, classifies and decides, and code does the rest. Measured in hours reclaimed.
- [Private & on-prem AI](https://ai.arcvs.tech/en/services/private-ai): Open-weight models on your cloud or hardware. Your prompts and documents never leave your perimeter.
- [AI security](https://ai.arcvs.tech/en/services/ai-security): Red teaming for LLM apps and agents: prompt injection, data leakage, excessive agency, insecure tools.
- [OSINT & security audits](https://ai.arcvs.tech/en/services/osint-security-audits): Your exposure, seen from outside: breaches, leaks, forgotten assets, CVEs. Then authorized testing and fixes.

## Contact

- WhatsApp: +5532999143361 (https://wa.me/5532999143361)
- Telegram: @w1ldcrow (https://t.me/w1ldcrow)
- Email: contact@arcvs.tech
- Contact section: https://ai.arcvs.tech/en#contact
- Parent studio: https://www.arcvs.tech