Orchestrate AI agents that work together.
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 per-agent model choice built in. Not DIY.
Managed orchestration with quality gates (auto/review/approval), communication rules (allow/deny/route_through), per-agent model choice, and personal companion agents. Define workflows in natural language, not infrastructure code.
You focus on the work. Sprigr runs the paperwork.
- Workflow orchestration
- Quality gates
- Companion agents
- Full audit trail
Everything you need for multi-agent orchestration
Sprigr handles the coordination, security, and infrastructure so you can focus on what your agents should accomplish.
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Quality gates
Four gate types for every workflow step: auto, review, approval, and decision. Auto-run routine tasks, flag sensitive steps for review, pause for explicit approval, or ask a named person to choose between options. Every gate decision is logged.
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Communication rules
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.
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Model choice per agent
Pick Opus, Sonnet, or Haiku for each agent, or leave it on Auto and let Sprigr choose a tier for the task. Cheap models for routine work, stronger models for reasoning.
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Companion agents
Every user gets a personal companion agent bound to their own identity and connections. It works in that person's inbox, calendar, and apps as them, and hands work to shared standalone agents in a team room.
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Credential isolation
Company-wide integrations (the Slack bot, a GitHub token, simPRO) are shared by every agent you give them to. Per-user apps (Gmail, Google Workspace, Gorgias) are bound to the connecting user, so an agent only ever acts as the person it belongs to. Nothing is stored in plaintext.
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Workflow audit trail
Every agent action, every message, every quality gate decision is logged with timestamps. Complete visibility into multi-agent workflows. Export-ready for compliance.
The numbers behind managed orchestration
Gates, audit trails, and per-user connections by default.
- 4
quality gate types
Auto (no human), review (flagged), approval (paused until a human approves), decision (a named person chooses between options). Set the gate per workflow step based on risk.
- 100%
of agent actions logged
Every message, tool call, and gate decision captured with timestamp and agent identifier. Audit-ready by default.
- Per user
connections for personal apps
Gmail, Google Workspace, and Gorgias connect per user, so an agent only acts as the person it is bound to. Company-wide integrations are shared by design and encrypted at rest.
How it works
Four steps to multi-agent workflows that run securely and autonomously.
- 01
Define your agents
Create specialized agents with distinct roles. A research agent, a writer agent, a reviewer agent. Each with their own tools and permissions.
- 02
Set communication rules
Control how agents interact. Allow direct communication, deny certain paths, or route messages through supervisor agents for oversight.
- 03
Add quality gates
Choose auto-approval for routine tasks, human review for sensitive operations, or full approval gates for high-stakes decisions.
- 04
Deploy and monitor
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 →Questions
What is multi-agent workflow orchestration?
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 manages the infrastructure, communication, and quality control.
How do agents communicate with each other?
Agents communicate through Sprigr'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.
What are quality gates?
Quality gates are checkpoints in multi-agent workflows. Four types: auto (the agent proceeds without human intervention), review (output is flagged for human review but work continues), approval (work pauses until a human approves), and decision (work pauses until a named person chooses between options). You set the gate per workflow step based on risk and sensitivity.
How is this different from CrewAI or LangGraph?
CrewAI and LangGraph are open-source frameworks that require you to host, secure, and manage your own infrastructure. Sprigr is a managed platform with per-tenant data isolation, encrypted credentials, communication rules, and quality gates built in. You define what agents should do. We handle the infrastructure, security, and orchestration.
Can I control which credentials each agent accesses?
At the integration level, yes. You choose which agents are given each integration. Company-wide integrations are shared by every agent that has them; per-user apps are bound to the connecting user and never shared between users. Credentials are encrypted at rest and decrypted only at runtime, never stored in plaintext.
Ready to orchestrate AI agent teams?
Multi-agent workflows with security built in.