The runtime your agents run on.
Agentic infrastructure is the system an AI agent needs around the model to do real work in production: a runtime that keeps state and survives failures, a tool layer with scoped permissions, a context layer that feeds it the right information, and evaluations that prove it works. RXY Studio designs, builds and operates that system for companies.
What is agentic infrastructure?
A model on its own answers a prompt. An agent runs a loop: it reads the task, picks a tool, looks at the result and decides the next step, until the job is done. Anthropic describes agents as systems where the model dynamically directs its own process and tool usage. Everything that loop depends on, apart from the model, is agentic infrastructure.
In a demo that infrastructure is a script and an API key. In production it has to hold state for hours, retry safely, ask a human before it spends money, and leave a trace of every decision.
What are the layers of agentic infrastructure?
| Layer | What it does | What we build |
|---|---|---|
| Runtime | Runs the agent loop, keeps state, recovers from failures | Durable execution, retries and timeouts, human approval steps, sandboxed code execution |
| Tool layer | Connects the agent to the systems it can read and change | Typed tools, Model Context Protocol servers, scoped credentials, an audit log of every call |
| Context layer | Gives the model the right information at each step | Retrieval pipelines, context assembly, session and long-term memory, access control on what is retrieved |
| Evaluation layer | Proves the agent works before and after release | Eval suites built from real cases, regression gates in CI, tracing, online quality monitoring |
| Guardrails | Limits what can go wrong | Least-privilege permissions, input and output checks, spend and rate limits, defenses against prompt injection |
Why do agent projects fail in production?
Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. Each of those causes is an infrastructure gap: no cost control around inference, no evaluation that ties the agent to a business result, no permission model.
- State is lost when a step fails, so long tasks restart from zero or stop silently.
- Tools are too broad: one credential that can read and write everything.
- There is no eval suite, so every prompt change is a guess.
- Nobody can say what the agent did, why it did it, or what it cost.
How does RXY Studio build it?
- Map the work: the task, the systems it touches and the actions that need a human.
- Define success as numbers: task success rate, cost per task, time to completion.
- Build the eval suite first, from real cases, so every later change is measured.
- Build the runtime, tools and context layer around the simplest agent design that passes.
- Ship behind approvals and widen autonomy as the numbers hold.
- Operate it: tracing, alerts, cost budgets and scheduled eval runs.
What do you build with?
We are not tied to one framework or vendor. Typical building blocks are models from Anthropic, OpenAI, Google and open-weight families, the Model Context Protocol for tools, durable workflow engines such as Temporal for long-running execution, OpenTelemetry for tracing, and Postgres with vector search for state and retrieval. We choose per system and document why.
What you get
Agent runtime
Durable, observable execution with approvals and limits.
Tool layer
Typed, permissioned tools and MCP servers over your systems.
Context layer
Retrieval and memory with access control.
Eval suite
Tests built from real cases, run on every change.
Runbook
Tracing, alerts, budgets and on-call procedures.
Frequently asked questions
What is the difference between an AI agent and agentic infrastructure?
The agent is the model in a loop with tools. Agentic infrastructure is everything that loop needs to run in production: runtime, tool layer, context, evaluations and guardrails.
Do we need an agent framework or a workflow engine?
It depends on the task. Short, single-step agents often need neither. Long-running, multi-step work benefits from durable execution so state survives failures. We pick the simplest design that passes the evals.
Can agents act on our internal systems safely?
Yes, with least-privilege tools: each agent gets only the actions it needs, sensitive actions require approval and every call is logged. OWASP lists excessive agency among the top risks for LLM applications, so permissions are designed first.
How do you measure whether an agent works?
Against an eval suite built from real cases, with task success rate, cost per task and latency tracked on every release and in production.
Do you operate what you build?
Yes. We run the system with tracing, alerts and cost budgets, or hand it over to your team with a runbook.