No-Code First, Low-Code Optional
Launch quickly with visual no-code setup, then add low-code API control only as workflows grow more complex. Every dashboard feature is also available through typed REST endpoints and OpenAPI docs.
No-Code AI Agents for Business Workflows
Build and deploy AI agents and multi-agent teams visually, without writing code.
Sistava is a no-code platform for building and deploying AI agents and multi-agent teams that automate business workflows. Start with visual configuration, then add optional low-code APIs, webhooks, MCP, and A2A only when you need deeper custom control.
Overview
The platform is designed for teams that want speed first and control when needed. Operations teams can launch workflows with no-code configuration, while engineering teams can layer low-code API logic only where custom behavior is required. Every action in the dashboard, from hiring employees to assigning skills to sending messages, is backed by a REST API endpoint.
Beyond basic CRUD operations, the platform supports three communication protocols. Webhooks let you trigger AI employee actions from any external event. The Model Context Protocol (MCP) provides a standardized way for AI employees to use external tools. The Agent-to-Agent (A2A) protocol enables cross-system agent orchestration.
SDKs are available for Python, TypeScript, and other popular languages. Each SDK wraps the REST API with typed interfaces, automatic retry logic, and streaming support for real-time responses.
Before / After
- Before: Building a custom AI agent from scratch takes 3-6 months of engineering time, and you still end up maintaining prompt infrastructure, tool orchestration, and memory systems.
After: Send a single API request to hire a fully configured AI employee with skills, tools, and memory already wired up. Ship in days, not quarters. - Before: Every AI provider has a different API shape, authentication scheme, and response format. Switching providers means rewriting your integration layer.
After: One unified API across all AI models. Switch the underlying model with a config change. Your integration code never changes. - Before: Connecting your AI system to external tools requires custom middleware, auth token management, and error handling for each integration.
After: MCP protocol support means your AI employees access tools through a standardized interface. Add a new tool by pointing to its MCP server. - Before: Coordinating multiple AI agents across different services means building custom message routing, state management, and failure recovery.
After: A2A protocol handles agent-to-agent communication natively. Your AI employees discover, negotiate with, and delegate to other agents automatically.
Benefits
Inbound and Outbound Webhooks
Trigger AI employee actions from external events with inbound webhooks. Receive real-time notifications when employees complete tasks, encounter errors, or need approval with outbound webhooks. Signed payloads with retry logic.
MCP Protocol Support
Expose your AI employees as MCP servers so other AI systems access their capabilities. Connect external MCP servers so your employees use third-party tools. Bidirectional tool sharing through one protocol.
A2A Agent Protocol
Your AI employees participate in multi-agent networks using the Agent-to-Agent protocol. They publish their capabilities, discover other agents, and collaborate on tasks across organizational boundaries.
SDKs for Every Language
Official SDKs for Python, TypeScript, Go, and Ruby. Each SDK includes typed models, streaming support, async/await patterns, and automatic token refresh. Install from your package manager and start building in minutes.
Real-Time Streaming
Stream AI employee responses token by token through WebSocket connections or Server-Sent Events. Build responsive UIs that show typing indicators and incremental responses, just like the built-in chat.
How It Works
- Get Your API Key — Generate an API key from your workspace settings. Each key is scoped to a specific workspace and inherits that workspace's permissions and billing. Rotate keys without downtime using key pairs.
- Send Your First Request — Use the REST API to hire an employee, assign skills, or send a message. Every endpoint returns structured JSON with consistent error codes. The OpenAPI spec provides auto-generated client libraries for any language.
- Connect Your Systems — Set up webhooks to trigger employee actions from your existing tools, CRM events, support tickets, or deployment pipelines. Configure outbound webhooks to push employee outputs back into your systems.
- Scale to Production — Rate limits scale with your plan. Built-in retry logic handles transient failures. Idempotency keys prevent duplicate operations. Monitor API usage and latency through the developer dashboard.
Comparison
| Dimension | Traditional | With Sista |
|---|---|---|
| Time to first AI integration | 3-6 months of custom development, prompt engineering, and infrastructure setup | First API call within 10 minutes. Production-ready integration within a day |
| Tool connectivity | Custom middleware for each tool. Maintain auth tokens, handle rate limits, build error recovery per integration | MCP protocol provides a universal tool interface. Add tools by URL, not by code |
| Multi-agent coordination | Build custom message queues, state machines, and failure recovery between AI services | A2A protocol handles discovery, negotiation, and task delegation between agents natively |
| Model flexibility | Locked into one provider API. Switching means rewriting prompt formats, response parsing, and tool calling | One API across all models. Switch providers with a configuration change, no code updates |
| Maintenance burden | Own team maintains prompt templates, memory systems, tool orchestration, and model upgrades | Platform handles infrastructure. Your team focuses on business logic and integration |
| Cost structure | Engineering salaries for AI infrastructure team plus cloud compute plus per-token model costs | Pay per use with transparent pricing. No infrastructure team required |
FAQ
Do I need developers to use this?
No. The default workflow is no-code. You can hire AI employees, assign skills, connect tools, and automate work from the dashboard. Developers are optional and only needed for custom low-code extensions.
What programming languages does the Sistava API support?
The REST API works with any language that makes HTTP requests. Official SDKs are available for Python, TypeScript, Go, and Ruby with typed interfaces, streaming support, and automatic retry logic. Community SDKs exist for Java, C#, and PHP.
How do I authenticate API requests?
Generate an API key from your workspace settings. Include it as a Bearer token in the Authorization header. Keys are scoped to a specific workspace and inherit its permissions. You can create multiple keys with different scopes and rotate them without downtime.
What is the MCP protocol and why should I use it?
The Model Context Protocol (MCP) is an open standard for connecting AI systems to external tools. Instead of building custom integrations for each tool, you expose or consume tools through a unified interface. Your AI employees use MCP to access databases, APIs, file systems, and any other tool that implements the protocol.
What are the API rate limits?
Rate limits scale with your plan. The free tier allows 60 requests per minute. Paid plans start at 600 requests per minute and scale to 6,000+ for enterprise accounts. Burst capacity handles traffic spikes. Rate limit headers are included in every response so you can implement client-side throttling.
Can I use the API to build a white-label product?
Yes. The API gives you full control over hiring, configuring, and interacting with AI employees. You can build custom interfaces, embed AI employees in your own product, and present them under your own brand. Enterprise plans include white-label support with custom domains and branding.
How does A2A differ from MCP?
MCP is for tool access: an AI agent using a database, calling an API, or reading a file system. A2A is for agent-to-agent collaboration: one AI employee delegating a subtask to another, negotiating task boundaries, or coordinating multi-step workflows across organizations. They are complementary protocols.