Multi-agent workflow orchestration with managed infrastructure. Deploy specialized agents that hand off work, check each other's output, and collaborate on complex objectives, with quality gates, communication rules, and smart routing built in. Not DIY.
Managed orchestration with quality gates (auto/review/approval), communication rules (allow/deny/route_through), smart model routing, and companion agents. Define workflows in natural language, not infrastructure code.
You focus on the work. Sprigr runs the paperwork.
Sprigr Team handles the coordination, security, and infrastructure so you can focus on what your agents should accomplish.
Three levels of oversight for every workflow step. Auto-approve routine tasks, flag sensitive operations for review, require explicit approval for high-stakes decisions. Every gate decision is logged.
Control exactly how agents talk to each other. Allow direct communication, deny certain paths, or route messages through supervisor agents. Prevent data leaks by design, not by policy.
Automatically route tasks to the right AI model. Use fast, cheap models for simple tasks and powerful models for complex reasoning. Reduce costs without sacrificing quality.
Attach specialized helper agents to any primary agent. A code reviewer that checks every code change. A compliance checker that validates every filing against your policies. Always-on quality assurance.
Each agent in a workflow receives only the credentials it needs. The research agent gets read-only API access. The deployment agent gets write access. Zero over-provisioning.
Every agent action, every message, every quality gate decision is logged with timestamps. Complete visibility into multi-agent workflows. Export-ready for compliance.
Gates, audit trails, and credential isolation by default.
Auto (no human), review (flagged), approval (paused until human approves). Set the gate level per workflow step based on risk.
Every message, tool call, and gate decision captured with timestamp and agent identifier. Audit-ready by default.
Each agent receives only what it needs. Encrypted at rest, runtime injection, no cross-agent credential sharing.
Four steps to multi-agent workflows that run securely and autonomously.
Create specialized agents with distinct roles. A research agent, a writer agent, a reviewer agent. Each with their own tools and permissions.
Control how agents interact. Allow direct communication, deny certain paths, or route messages through supervisor agents for oversight.
Choose auto-approval for routine tasks, human review for sensitive operations, or full approval gates for high-stakes decisions.
Agents collaborate autonomously within your defined boundaries. Monitor progress, review audit trails, and adjust as needed.
Multi-agent workflows multiply the attack surface. That's why every agent runs in isolated infrastructure with encrypted credentials.
Read the security deep-dive →It's a system where multiple AI agents with different specializations collaborate on complex tasks. Instead of one general-purpose agent, you deploy a team of specialists, a researcher, a writer, a reviewer, that hand off work and check each other's output. Sprigr Team manages the infrastructure, communication, and quality control.
Agents communicate through Sprigr Team's managed message pipeline. You define communication rules that control which agents can talk to each other, what information they can share, and whether messages need supervisor approval. All communication is logged and auditable.
Quality gates are checkpoints in multi-agent workflows. Three levels: auto (agent proceeds without human intervention), review (output is flagged for human review but work continues), and approval (work pauses until a human approves). You set the gate level per workflow step based on risk and sensitivity.
CrewAI and LangGraph are open-source frameworks that require you to host, secure, and manage your own infrastructure. Sprigr Team is a managed platform with physical data isolation, encrypted credentials, communication rules, and quality gates built in. You define what agents should do. We handle the infrastructure, security, and orchestration.
Yes. Each agent in a workflow receives only the specific credentials authorized for its role. A research agent might get read-only API access while a deployment agent gets write access. Credentials are encrypted at rest and injected at runtime, never stored in plaintext or shared between agents.
Enterprise-grade multi-agent workflows with security built in.