AI Agent Orchestration EngineEnterprise-ready · multi-tenant · MIT-licensed

Build, Deploy &

Orchestrate AI Agents

One open-source platform for agents, pipelines, and ontologies. Describe what you need in plain English and the builder assembles the agent for you — or drag-and-drop your own from 100+ built-in tools, uploaded code, trained ML models, and sandboxed jobs. Atlas turns your documents into a typed graph that agents traverse like citations. Policy-gated execution with moderation, DLP, RBAC, and full observability from day one.

AI-Powered Builder

Describe it, the platform builds it

Bring-Your-Own Code

Upload a repo, get a tool

100+ Built-in Tools

Plus MCP + sandboxed jobs

Moderation + DLP + RBAC

Policy-gated execution path

0

Pre-Built Agents

0

Built-in Tools

0

Test Suites

Abenix

Create your account or sign in

Quick Access:

256-bit EncryptionJWT AuthRBAC Enabled

Platform Capabilities

Everything you need to ship production-grade AI agents — builder, runtime, tools, knowledge, observability, and governance — in one platform.

Unique to Abenix

AI-Powered Builder

Describe what you need in plain English. The builder generates complete agents or pipelines with tools, parameters, and configurations — ready to execute in seconds.

Unique to Abenix

Bring-Your-Own-Code Tools

Upload a zip or point at a git repo — Python, Node, Go, Rust, Ruby, Java, Perl. The analyzer detects the language, runs it in a sandbox, and exposes it to every agent as a first-class tool with schema discovery and a build cache.

Unique to Abenix

Deploy ML Models as Tools

Upload scikit-learn, XGBoost, or ONNX models. The platform introspects the feature set, wraps inference in a sandboxed job, and exposes the model to agents through the same tool registry your Python code uses.

Moderation + DLP + RBAC

Pre-LLM and post-LLM moderation gates on every execution. DLP scans input for PII in detect / mask / block modes. Per-user resource isolation via polymorphic ResourceShare. Every agent inherits the tenant's policy.

Visual Pipeline Builder

Drag-and-drop canvas with 100+ built-in tools, switch routing, merge nodes, error branches, per-node timeouts, while loops, and for-each parallel iteration.

Multi-Model + Multi-Agent

Route across Claude, GPT, Gemini. Chain agents as pipeline steps. LLM-powered dynamic routing with confidence-gated branching. Per-agent runtime pools with KEDA queue-depth autoscaling.

MCP + 100+ Built-in Tools

Financial, risk, KYC/AML, market data, weather, patents, Twilio, Plotly charts, browser automation, PII redactor, memory, moderation, and more. Plus full MCP protocol for unlimited extensibility.

Knowledge Engine — Projects, Ontology, Hybrid Search

Organise collections into projects, control access per agent and per user, author a domain ontology that constrains entity extraction, and explore correlations across collections. Hybrid retrieval over Pinecone or pgvector plus Neo4j graph traversal. Bootstrap endpoint for standalone-app integrations.

Meeting Primitives

Eight tools for live-meeting agents: join, listen, speak, post-chat, leave, plus persona RAG, defer-to-human, and scope gating. Sub-3s response latency. Meetings become an agent execution surface, not a separate product.

Live Debug & Flight Recorder

Real-time SSE streaming with per-node status. Full execution traces, waterfall visualization, replay from any node. Prometheus /metrics on every pod, Grafana dashboards, and /alerts page grouped by failure_code.

Wave-2 Scale: NATS + KEDA + Multi-Pool

Per-agent runtime pools with KEDA queue-depth scaling. NATS JetStream transport, Redis execution bus, stale-sweeper with advisory locks. Validated at 500+ concurrent workflows without regression.

Enterprise: Drift, Cost, HITL

Statistical drift detection, per-execution cost budgets, human-in-the-loop approval gates, moderation queue, Slack / email / webhook alerts, and circuit breakers with adaptive retry.

How It Works

Three steps from idea to production-ready AI agent.

01

Design

Describe your agent in plain English or open the drag-and-drop builder. Pick a model, wire in tools, and preview the generated pipeline before saving.

02

Connect

Upload a code repo or ML model, attach MCP servers, link knowledge bases, and configure per-tool parameter defaults. Schemas, examples, and build caches are inferred for you.

03

Deploy

One-click publish to a private tenant or share with your team. Autoscaling runtime pools, moderation / DLP gates, Prometheus + Grafana, and per-agent cost budgets ship on by default.