Jenova Agent API: One API to Power Your Entire Agent Backend


2026-07-21


Building production AI agents still means stitching together LLM provider APIs, orchestration frameworks, vector databases, memory stores, tool servers, and the infrastructure to keep it all running under real traffic. The Jenova Agent API eliminates this entirely — one API that replaces your entire DIY agent stack with the first fully-managed agent backend on the market.

✅ Define custom agents in minutes — instructions, models, knowledge bases, tools ✅ Any model from OpenAI, Anthropic, Google, xAI, DeepSeek — one endpoint, no lock-in ✅ Persistent memory, managed storage, and built-in RAG — zero infrastructure to provision ✅ Battle-tested orchestration and MCP tool integrations from day one

Jenova Agent API — One API to Power Your Entire Agent Backend

You bring the domain expertise. The API handles everything else. To understand why that matters, look at what it actually takes to ship a production agent today — and why the current landscape of frameworks, SDKs, and managed services still leaves the hardest problems unsolved.

Quick Answer: What Is the Jenova Agent API?

The Jenova Agent API is a fully-managed agent backend that lets developers build, configure, and deploy custom AI agents through a single integration — replacing DIY orchestration, model routing, memory, storage, RAG, and tool infrastructure.

Key capabilities:

  • Build custom agents with specific instructions, model selection, private knowledge bases, and tool configurations
  • Agent orchestration coordinating models, tools, memory, and retrieval automatically
  • Multi-model access across OpenAI, Anthropic, Google, xAI, DeepSeek — no vendor lock-in
  • Unlimited conversation memory and persistent context built into every session
  • Fully-managed relational and vector databases with built-in RAG
  • Unlimited MCP tool integrations ready out of the box

The Problem: Shipping AI Agents Is an Infrastructure Problem

The AI agents market is projected to grow from $10.9 billion to $182.9 billion by 2033 at a CAGR of 49.6%. With over 170 AI agent startups mapped across 26 categories and 51% of organizations already using agents in some capacity, demand is not the bottleneck. Delivery is.

Getting agents from prototype to production is where engineering teams hit the wall:

  • Orchestration complexity — coordinating models, tools, memory, and retrieval across multi-step workflows
  • Model vendor fragmentation — managing separate integrations, SDKs, and billing for every LLM provider
  • Context and memory management — building external state stores for conversations that persist
  • RAG pipeline engineering — provisioning vector databases, tuning retrieval, managing embedding pipelines
  • Tool integration overhead — wiring MCP servers, API wrappers, and auth flows for every external service
  • Time-to-production — months of infrastructure work before the agent does anything useful

AI agent architecture diagram illustrating the complexity of coordinating models, tools, memory systems, and external integrations

The Gap Between Tooling and Production

AI agents fail between 70% and 95% of the time in production environments, depending on task complexity

The cause isn't model quality — it's everything around the model. Broken handoffs between steps, context drift in long-running workflows, and silent failures from unhandled API timeouts compound across every stage of execution.

The current tooling landscape offers partial solutions at each layer, but nothing covers the full stack:

An estimated 88% of enterprise AI agents that work in demos fail when deployed to real workflows

As Kearney's analysis of the agentic AI market notes, building agentic capabilities from scratch requires "highly specialized technical talent that is scarce, expensive, and difficult to recruit". The infrastructure market remains nascent, and most teams are stuck choosing between months of DIY infrastructure work or single-vendor lock-in.

This is exactly the problem the Jenova Agent API was built to eliminate.

The Solution: The Jenova Agent API

The Jenova Agent API is the first fully-managed agent backend — one API that handles orchestration, model routing, memory, storage, RAG, and tool integration so you can focus on what your agent does, not on keeping infrastructure running. Define your agent's behavior, attach your knowledge, pick your model, and deploy — the rest is handled.

DIY Agent StackJenova Agent API
Integrate 3–5 LLM provider APIs separatelyOne endpoint, any model — OpenAI, Anthropic, Google, xAI, DeepSeek
Build orchestration with LangGraph / CrewAI / AutoGenBattle-tested orchestration built in
Provision and manage vector databases for RAGFully-managed vector storage with built-in RAG
Implement external memory and state managementUnlimited persistent memory per session, zero config
Wire up MCP servers and tool integrations manuallyPlatform-native MCP with unlimited tool connections
Weeks to define, test, and iterate on agent behaviorCustom agent creation in minutes
Build, deploy, and scale your own infrastructureProduction-grade managed infrastructure

Custom Agents in Minutes

This is the core of the developer experience. Instead of months of pipeline engineering to get a specialized agent running, you define four things:

  • Instructions — the behavioral rules, domain expertise, and response guidelines that shape your agent's personality and capabilities
  • Model — choose the optimal model for your use case from OpenAI, Anthropic, Google, xAI, DeepSeek, or others. Switch anytime without code changes.
  • Knowledge base — upload documents, product specs, internal wikis, compliance guides — anything your agent needs for domain-specific reasoning. RAG is handled automatically.
  • Tools — connect MCP servers and platform-native integrations. Gmail, Calendar, Search, Notion, or any custom MCP endpoint your agent needs to interact with.

You bring the domain expertise. The API handles orchestration, retrieval, memory, and infrastructure. The agent you define is immediately accessible through a standard REST endpoint — production-ready from the first call.

Built-In Orchestration

Every API call routes through a battle-tested orchestration layer that coordinates models, tools, memory, and retrieval automatically. Multi-step workflows, tool calls, retrieval-augmented reasoning, and error recovery are handled by the platform — not by custom code in your application. No framework installation, no graph definitions, no state machine boilerplate. The same orchestration engine is production-tested with 30,000+ users in 70+ countries.

Any Model, One Endpoint

Access models from OpenAI, Anthropic, Google, xAI, DeepSeek, and more through a single integration. Assign different models to different agents based on the task — a reasoning-heavy agent on one provider, a fast-response agent on another — all through the same endpoint. When new models release, they're available without migration. No separate SDKs, no per-provider auth flows, no vendor lock-in.

Persistent Memory and Context

Every session includes unlimited conversation memory and persistent context. Your agent remembers past interactions, user preferences, and ongoing projects without external state stores. No Redis, no session databases, no cache invalidation logic. Memory is built into the infrastructure layer and scales automatically.

Fully-Managed Storage and Built-In RAG

The platform provides managed relational and vector databases. Upload documents to your agent's knowledge base, and retrieval-augmented reasoning works automatically — chunking, embedding, indexing, and retrieval are handled without configuration. No vector database provisioning, no embedding pipeline maintenance, no retrieval threshold tuning.

MCP Tools Out of the Box

Connect external services through platform-native MCP support. Gmail, Google Calendar, Drive, Search, Notion, and any remote MCP server are available without custom integration code. Define which tools your agent can access, and the orchestration layer handles invocation, authentication, and response handling.

Here's what a request looks like:

bash
curl -X POST https://api.jenova.ai/v1/chat \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": "your-custom-agent-slug",
    "messages": [
      {
        "role": "user",
        "content": "Analyze this quarter'\''s support tickets and identify the top three churn risk patterns"
      }
    ]
  }'

One call. Orchestration, model routing, memory, knowledge base retrieval, and tool access happen behind the endpoint. The full documentation covers authentication, endpoints, request/response schemas, and custom agent configuration.

vs LangChain and LangGraph

LangChain and LangGraph are the most widely adopted open-source tools for building AI agents. LangGraph provides a graph-based runtime with explicit state management and checkpointing — powerful primitives for complex, branching workflows. But they're frameworks, not managed services, and that distinction carries real operational cost:

LangGraph's explicit checkpointing and graph model are genuinely powerful for developers who need granular control over complex workflows and are willing to own the infrastructure. But for teams that want to ship agents — not build and operate agent infrastructure — the Jenova Agent API delivers orchestration, memory, RAG, and model routing as a managed service. No graph definitions, no container orchestration, no vector database provisioning.

vs OpenAI Agents SDK

The OpenAI Agents SDK is a lightweight, open-source framework built around four primitives: Agents, Tools, Handoffs, and Guardrails. It ships with built-in tracing, works with 100+ LLMs via the Chat Completions API, and gets developers running quickly with just a few lines of code.

But the SDK is intentionally incomplete — by design:

The SDK's strength is simplicity and fast prototyping. But its design "shifts responsibility to the developer when advanced behaviors are required". The Jenova Agent API is effectively what you'd have to build on top of the Agents SDK — memory, RAG, managed storage, multi-model routing, and production infrastructure — delivered as a managed service from the first API call.

vs Claude Managed Agents

Claude Managed Agents, launched in public beta in April 2026, is the closest existing analog to a managed agent backend. Anthropic runs the execution loop, handles sandboxing, session persistence, context compaction, and crash recovery. It removes real operational burden — and is particularly strong for long-running, async workloads.

But it comes with hard constraints:

The Jenova Agent API delivers the managed infrastructure benefit of Claude Managed Agents — without the single-vendor lock-in. Run agents on OpenAI, Anthropic, Google, xAI, or DeepSeek through the same endpoint. Switch models per agent or per request. No vendor dependency, no single-provider risk.

The Full Picture

CapabilityJenova Agent APILangChain / LangGraphOpenAI Agents SDKClaude Managed Agents
InfrastructureFully managedSelf-hostedSelf-hostedAnthropic-managed
Model supportOpenAI, Anthropic, Google, xAI, DeepSeek + moreAny (manual integration)100+ via Chat Completions (OpenAI-optimized)Claude only
Built-in memory✅ Unlimited, persistent❌ Build your own❌ Session only✅ Server-side
Built-in RAG✅ Managed vector storage❌ Build your own❌ Not included❌ Limited
Orchestration✅ Automatic✅ Graph-based (manual setup)✅ Basic loop✅ Managed harness
MCP tools✅ Platform-native❌ Build your own❌ OpenAI tools only✅ Supported
Custom agentsMinutes (instructions + model + knowledge + tools)Hours to days (code + infra)Minutes (code only, no infra)Minutes (config, Claude only)
Vendor lock-inNone — switch models anytimeLow (Apache 2.0)Medium (hosted tools lock to OpenAI)High (Claude + Anthropic infra only)
Production statusTested with 30K+ usersDepends on your infraDepends on your infraPublic beta

What You Can Build

The power of custom agents is that you define the expertise. The API provides the infrastructure to run it. Here's what that looks like across domains:

Customer-Facing AI Features

Build an agent that knows your product inside out — upload your documentation, FAQs, and troubleshooting guides as the knowledge base, define response guidelines that match your brand voice, and connect it to your CRM and ticketing system via MCP. Your customers interact with a support agent that actually understands your product, maintains context across conversations, and takes action on their behalf. No ML team. No retrieval pipeline to build.

Internal Operations Automation

Create agents that handle internal workflows — an onboarding agent with your company's policies and procedures as its knowledge base, a reporting agent connected to your data tools, or a compliance review agent with your regulatory docs loaded. Each one is a custom agent definition and an API call — not a six-month engineering project.

Domain-Specific Analysis

Build agents for specialized analysis by pairing the right model with the right knowledge base. A financial modeling agent with your valuation frameworks. A legal review agent with your contract templates and clause library. A research agent with your proprietary datasets. The domain expertise lives in your instructions and knowledge base — the API handles the reasoning, retrieval, and multi-step execution.

Multi-Agent Workflows

Compose specialized agents that each handle one stage of a larger pipeline. One agent ingests and structures data, another analyzes it, a third generates the output — each with its own optimized model, knowledge base, and tool configuration. Orchestrate across agents through your application logic while each individual agent runs on fully-managed infrastructure.

Get your API key free at jenova.ai/platform and start building.

How It Works

From zero to a production-ready custom agent in four steps:

Step 1: Get Your API Key

Create an account at jenova.ai/platform and generate your API key. One key, one integration point — no separate credentials per model provider or service.

Step 2: Build Your Custom Agent

Define your agent's behavior: write the instructions that shape its expertise, select the model that fits your use case, upload documents for its knowledge base, and configure tool access. The agent is identified by a slug and immediately accessible through the API.

json
{
  "agent": "your-custom-agent-slug",
  "instructions": "Your behavioral rules and domain expertise...",
  "model": "preferred-model",
  "knowledge_base": ["uploaded-documents"],
  "tools": ["connected-mcp-servers"]
}

Step 3: Add Credits

Add API credits to your account. Credits are consumed based on usage — the models, tools, and storage your agents use per request. No fixed licenses, no minimum commitments.

Step 4: Make Your First Call

Send a request and receive a fully orchestrated response:

bash
curl -X POST https://api.jenova.ai/v1/chat \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": "your-custom-agent-slug",
    "messages": [
      {
        "role": "user",
        "content": "Review this contract for non-standard liability clauses and summarize the key risks"
      }
    ]
  }'

The agent handles multi-step reasoning, knowledge base retrieval, tool calls, and response generation automatically. Full endpoint reference, authentication details, and response schemas available in the API documentation.

Results & Use Cases

🚀 EdTech Startup Shipping AI Tutoring in Days, Not Months

Scenario: A 5-person edtech startup needs adaptive AI tutoring in their learning platform before the school year starts.

Traditional Approach: 2–3 months building orchestration, integrating an LLM, setting up a vector database for curriculum documents, building a memory layer for student progress tracking, and deploying infrastructure. Estimated: $50K–$150K in engineering time and cloud services.

Jenova Agent API: The team creates a custom tutoring agent — uploads their curriculum as the knowledge base, defines pedagogical instructions for adaptive teaching, selects a reasoning-optimized model — and integrates it in a single sprint. Memory tracks each student's progress across sessions automatically.

  • Months of infrastructure work replaced by one agent definition and one integration
  • Built-in document storage and RAG for curriculum materials — no vector DB setup
  • Persistent memory tracks student progress without external state management

💼 SaaS Platform Adding AI Analysis Without an ML Team

Scenario: A B2B analytics platform wants to offer AI-powered insights to enterprise clients without hiring a dedicated ML engineering team.

Traditional Approach: Hire 3–4 ML engineers, spend 6+ months building a custom agent pipeline, maintain infrastructure indefinitely. Every model upgrade requires re-integration.

Jenova Agent API: The team builds a custom analysis agent with their domain frameworks as the knowledge base and connects it to their data sources via MCP. Deploy through the existing API. Switch models as better options emerge — no code changes.

  • No ML team required — domain expertise lives in the agent definition
  • Persistent memory retains client context across sessions automatically
  • Model-agnostic: upgrade to a better model by changing a configuration, not rewriting code

⚕️ Healthcare Company Deploying Specialized Agents Across Departments

Scenario: A health tech company needs different AI capabilities across three departments — clinical decision support, patient communication, and administrative operations — each with distinct compliance requirements and knowledge bases.

Traditional Approach: Build three separate agent pipelines, each with its own model integration, retrieval system, compliance guardrails, and deployment infrastructure. Triple the engineering, triple the maintenance.

Jenova Agent API: Create three custom agents on the same infrastructure — each with its own instructions, knowledge base, compliance rules, and tool configurations. All three deploy through the same endpoint. Shared infrastructure, isolated agent behavior.

  • One integration serves all three departments — no duplicated infrastructure
  • Each agent's knowledge base and instructions are fully isolated
  • Managed storage and RAG handle compliance documentation at scale

📱 Developer Eliminating the Rebuild Cycle

Scenario: A developer rebuilt their product's AI feature three times — once for each LLM provider migration. Each rebuild took 6 weeks and broke existing user workflows.

Traditional Approach: Tight coupling to a single model provider. Every provider switch requires code changes, regression testing, and redeployment across the entire stack. With Claude Managed Agents, migrating means rebuilding orchestration, sandboxing, and crash recovery from scratch — the runtime doesn't transfer.

Jenova Agent API: Build the agent once. When a better model releases, switch at the agent configuration level — no code changes, no redeployment, no broken workflows. The advantage over both DIY stacks and single-provider managed services isn't just launch speed — it's never rebuilding again.

  • Model-agnostic by design — switch providers without touching application code
  • One integration survives every model generation
  • Built-in memory retains user context and preferences regardless of model changes

Frequently Asked Questions

How much does the Jenova Agent API cost?

The API uses a credit-based model — you pay for what your agents consume based on the models, tools, and storage used per request. No fixed per-seat licenses, no minimum commitments. Add credits as needed and scale with usage. Detailed pricing is available at jenova.ai/platform.

What models are available through the API?

The Jenova Agent API provides access to models from OpenAI, Anthropic, Google, xAI, DeepSeek, and additional providers through a single endpoint. New models become available through the same integration as they release — no SDK updates or migration required. Assign different models to different agents based on the task.

How quickly can I build and deploy a custom agent?

Minutes. Define your agent's instructions, select a model, upload your knowledge base, configure tools, and the agent is immediately accessible through the API. There's no training step, no deployment pipeline, no infrastructure to provision. The bottleneck is defining what your agent should do — not making it run.

How is this different from LangChain, OpenAI Agents SDK, or Claude Managed Agents?

Open-source frameworks like LangChain and LangGraph give you orchestration primitives but require you to build, host, and scale everything yourself — memory, RAG, infrastructure, the full stack. The OpenAI Agents SDK is intentionally lightweight: no built-in memory, no RAG, no managed infrastructure. Claude Managed Agents handles the runtime but locks you into Claude models on Anthropic's infrastructure exclusively. The Jenova Agent API is the first fully-managed backend that combines model-agnostic flexibility, built-in memory and RAG, and production infrastructure — through one integration.

Is the API production-ready?

Yes. The API runs on production-grade managed infrastructure, backed by the same orchestration engine tested with over 30,000 users across 70+ countries. Stable endpoints, managed scaling, and zero infrastructure for you to maintain.

How difficult is the integration?

Four steps: get an API key, build your agent, add credits, make a call. The API follows standard REST conventions. If you can make an HTTP request, you can run a production agent. The full documentation covers authentication, endpoints, request/response formats, and agent configuration.

Conclusion

The gap between AI agent demos and production deployments isn't model intelligence — it's infrastructure. Orchestration, model routing, memory, RAG, and tool integration are the engineering work that stops most agent projects before they ship. Open-source frameworks give you building blocks but leave you owning the infrastructure. Provider SDKs are deliberately incomplete. Provider-managed services trade operational burden for single-vendor lock-in.

The Jenova Agent API is the first fully-managed backend that eliminates this entire set of tradeoffs — define your agent's expertise, attach your knowledge, pick any model, and deploy through one endpoint. Custom agents in minutes, not months. Any model, not one vendor. Managed infrastructure, not your problem.

Stop building agent infrastructure. Start shipping agents.

Get your API key and full reference at jenova.ai/platform/docs. Explore the platform at jenova.ai/platform. Learn more at Jenova.