Quality Gates
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.
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 smart routing built in. Not DIY.
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Quality gates
Communication rules
Smart routing
Complete audit trail
The DIY approach
Building your own orchestration means managing agent communication, error handling, retry logic, quality checks, credential passing, and monitoring across multiple agents. Weeks of infrastructure work before your first workflow runs.
Agent communication
Error handling
Retry logic
Credential passing
The Sprigr way
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.
Quality gates
Communication rules
Smart routing
Companion agents
How it works
Four steps to multi-agent workflows that run securely and autonomously.
1
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.
2
Set communication rules
Control how agents interact. Allow direct communication, deny certain paths, or route messages through supervisor agents for oversight.
3
Add quality gates
Choose auto-approval for routine tasks, human review for sensitive operations, or full approval gates for high-stakes decisions.
4
Deploy and monitor
Agents collaborate autonomously within your defined boundaries. Monitor progress, review audit trails, and adjust as needed.
Everything you need for multi-agent orchestration
Sprigr Team handles the coordination, security, and infrastructure so you can focus on what your agents should accomplish.
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.
Smart Model Routing
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.
Companion Agents
Attach specialized helper agents to any primary agent. A code reviewer that checks every code change. A compliance checker that validates every customer communication. Always-on quality assurance.
Credential Isolation
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.
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.
Multi-agent workflows multiply the attack surface. That’s why every agent runs in isolated infrastructure with encrypted credentials.
Frequently asked 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 Team manages the infrastructure, communication, and quality control.
How do agents communicate with each other?
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.
What are quality gates?
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.
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 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.
Can I control which credentials each agent accesses?
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.
Ready to orchestrate AI agent teams?
Enterprise-grade multi-agent workflows with security built in.
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