How does an agent runtime work?

An agent runtime receives requests, manages session state, executes the agent's steps, invokes tools and returns the response through the API. Idun Engine handles all of this for any LangGraph or ADK AI agent, machine learning and large language models included.

What is the difference between a runtime and an AI agent framework?

The framework (LangGraph, ADK) defines the logic. The runtime executes in production and takes care of orchestrating tools, state and security. Idun Engine handles stable execution. The two are complementary.

How do you secure an AI agent in production?

Enable guardrails on the runtime: PII detection, prompt injection defences and RBAC on tools. In every case, follow the OWASP Agentic Top 10 2026. Implement SSO/OIDC for the non-human identities of your production agents.

What are the best runtime tools for AI agents in 2026?

The open-source Idun platform (GPL v3, public and auditable code) provides a runtime for LangGraph and ADK. It includes state management, guardrails, observability and a standardised API. The platform deploys on your own servers, with no vendor lock-in.

How do you integrate an AI agent into a company?

Book a 30-minute discovery call with Idun Group. Use a structured runtime to standardise how your agents integrate with your workflows and IT systems, existing and future. Idun Group supports you from analysis through to operational agents.

What challenges do AI agents face in production?

The three main challenges: (1) multi-session state management, where handling state correctly is the most common pitfall, (2) insufficient observability for debugging, (3) AI Act compliance and OWASP risks. Rather than improvising, take concrete action: deploying a structured runtime solves all three. AI agents without a runtime accumulate technical debt. They are also a growing compliance risk.

AI agent runtime: components, security and the open-source choice

Q: What is an AI agent runtime?

An AI agent runtime is the execution layer that runs an agent in production. It handles the execution environment, state management, tool invocation and the API. The execution environment is what lets you build a production-ready AI agent with LangGraph or ADK, separate from the framework that defines the behavioural logic.

TL;DR: Without a production-ready runtime, your AI agent is already exposed to 3 of the 10 OWASP Agentic 2026 risks. An AI agent runtime rests on 6 components (state management, tool invocation, memory, observability, guardrails, standardised API). DIY vs PaaS vs open source compared: the open-source Idun platform (GPL v3, pip install idun-agent-engine) supports LangGraph and ADK with no vendor lock-in.

Without a production-ready runtime, your AI agent is already exposed to 3 of the 10 OWASP Agentic 2026 risks. You have a LangGraph or ADK agent running locally with your favourite LLM. Getting it into reliable production with persistent state management, dependable tool invocation and observability is another matter. An agent runtime is the execution environment that manages state, tools and security, which no framework does for you. Fast integration with pip install idun-agent-engine: your agent is production-ready in minutes. This guide covers the 6 components, the DIY vs PaaS vs open-source comparison (deployable across all your infrastructure) and 2026 compliance for enterprise AI agents.

What is an AI agent runtime? Definition and role

The fundamental distinction between framework and runtime

An agent runtime is the execution layer that runs your agent in production. LangGraph and ADK are frameworks: they define how an intelligent agent thinks and decides (decision loops, state transitions). The runtime is the layer underneath, where execution actually happens. The runtime is the execution: it exposes your agent as a reliable service with a stable execution environment, a swappable LLM, an interchangeable model, built-in MCP (Model Context Protocol) and native observability.

A developer building an agent with LangGraph writes the business logic, the complete agentic workflow. The runtime solves the production problems: persistence, retries, tracing, API versioning. Most projects fail because these infrastructure layers are missing. An application with a structured runtime scales across every deployment: all agents run with the same reliability.

The architecture of an AI agent runtime comes down to three stacked layers:

1. Framework (LangGraph / ADK): behavioural logic, decision loops, state transitions, prompts and tools, in other words what the agent does 2. Infrastructure layer: the bridge between the logic and actual execution, where the runtime takes over from the framework 3. Runtime (Idun Engine): 6 production-ready components, namely persistent state management, tool invocation with retries, OpenTelemetry observability, sub-10ms guardrails, multi-level memory and a standardised AG-UI/CopilotKit API

The framework decides. The runtime executes. The two are complementary, not interchangeable.

The 6 technical components of a production-ready runtime

A production-ready runtime rests on 6 building blocks. Deploying an LLM agent to production requires the orchestration of these 6 components:

1. State management: context persisted between sessions (in-memory, SQLite, PostgreSQL) 2. Tool invocation: calls to external tools with retries, timeouts and rate limiting 3. Persistent memory: handling all 3 levels, namely ephemeral, session and long-term 4. Observability: traces, logs and metrics in real time via OpenTelemetry 5. Guardrails: PII detection, prompt injection defences, topic restrictions 6. Standardised API: stable AG-UI and CopilotKit endpoints, compatible with any application

Human in the loop and human oversight rely on these 6 blocks. Orchestrating these components is the fundamental build of an enterprise runtime. An LLM model plugs into the runtime through a standard abstraction.