Introducing the Nebius Agents Blueprint: open architecture for production-ready AI agents

Most teams can build an agent. Getting one to run reliably in production is a different challenge entirely, and it’s a defining problem for 2026.

Last year, the defining question in AI was whether agents could work at all. That question has largely been answered. The questions today are operational: Can you run an agent system at scale, diagnose its failures, and make it measurably better over time? These aren’t model problems, they’re system problems, and the organizations figuring that out are pulling ahead.

Today we are introducing the Nebius Agents Blueprint, an open reference architecture for building, operating and continuously improving AI agents in production.

Agent failures are system failuresAgent failures are system failures

The failure pattern that we most often see isn’t about model failures, it’s about system failures.

The model performs exactly as designed, reasoning faithfully over whatever the harness provides. When the harness is wrong, the output is wrong. For example, when retrieval returns the wrong documents, when orchestration missequences tools, or when there’s no evaluation loop to catch drift, the output is wrong in ways that are hard to trace and harder to fix.

Three failure modes compound reliably in production:

• First, reliability: A 95% success rate at each step becomes roughly 60% task completion across a ten-step workflow.
• Second, cost predictability: Token spend has long tails; one poorly planned execution path can multiply your inference bill before anyone notices.
• Third, observability: When an agent fails, the trace is non-deterministic and often unreadable. Take away the evaluation loop, add stale retrieval, and you have a system that fails in the same ways indefinitely.

The challenge is no longer generating an answer. It’s generating reliable answers repeatedly, economically, and at scale. These are system problems, and they require system solutions.

System improvements outrun model upgradesSystem improvements outrun model upgrades

There’s a persistent assumption in the industry that better agent performance primarily comes from better models. Our experience suggests otherwise, and the research increasingly agrees: retrieval quality, orchestration strategy, grounding, and evaluation often have a larger impact than incremental model improvements.

We saw this ourselves. We did not post-train DeepSeek or Nemotron. Instead, we improved the system around them — better retrieval architecture, better orchestration, better evaluation — and that’s where the performance came from.

For teams building on open models, the implication is significant: the path forward isn’t waiting for the next release. It’s proactively building a better runtime and having the observability to know what’s working. That insight sits at the center of the Blueprint.

Nebius Agents Blueprint: six components, one composable stackNebius Agents Blueprint: six components, one composable stack

The Blueprint is two things: an open reference architecture that connects proven components at each layer of the agent stack, and runnable recipes with cloneable code from first agent to production-ready system.

• Inference — Nebius Token Factory: Dedicated endpoints, autoscaling, OpenAI-compatible API. 60+ open models. Data stays in your environment;
• Orchestration — LangChain Deep Agents: Multi-step workflows, persistent state, MCP-compatible tool connections;
• Observability — LangSmith: Every prompt, tool call, and retrieval step recorded. Trace any failure through a single execution record;
• Knowledge — Pinecone vector DB + Nexus: Pinecone Nexus compiles task-specific knowledge artifacts at index time, so agents work from prepared context instead of assembling it at query time. Pinecone vector search serves as a retrieval primitive underneath;
• Grounding — Tavily by Nebius: Real-time web retrieval with source reliability filtering;
• Simulation — Snowglobe by Guardrails AI: Hundreds of simulated tasks before deployment. Produces an eval dataset, fine-tuning data, and a QA regression suite from the same runs.

Every component is independently deployable, so you can adopt the full stack or integrate individual components into an existing system.

Real results: building a compliance agent using the BlueprintReal results: building a compliance agent using the Blueprint

To validate the architecture, we built a regulatory compliance audit agent capable of monitoring regulatory changes, assessing impact across a 200-SOP corpus spanning ten business units, evaluating requirements against 36 frameworks including FDA, HIPAA, GDPR, and the EU AI Act, and creating Jira tickets for every confirmed gap.

We ran the same workload across four configurations, progressing from a GPT-5.5-based prototype to a production-ready system on open models. The results revealed two distinct sources of improvement:

• Model swaps delivered the economic breakthrough: moving from GPT-5.5 to DeepSeek-V4-Pro cut costs by more than 70%-80% with no retraining;
• Harness changes delivered on quality: improvements to retrieval, orchestration, grounding, and evaluation drove precision and actionability gains that no model swap alone could have produced.

The highest-performing configuration used NVIDIA Nemotron Ultra, released last week and available now on Nebius Token Factory. As a 550B-parameter MoE model, purpose-built for agentic workloads and long-running reasoning tasks, it delivered the strongest balance of quality, efficiency and cost across the benchmark. The harness included Blueprint components for orchestration, retrieval, grounding, observability, simulation and inference infrastructure.

Results across the 120-task benchmark: 20% higher precision, 72% lower cost than the prototype using GPT-5.5 and base orchestration and retrieval. On the specific FDA audit task: 95% lower cost, 2.4× faster execution, lowest review burden of all configurations tested.

The open model wasn’t the limiting factor. The system around it was.

Read the full case study

From working to reliable to measurableFrom working to reliable to measurable

Production-ready means the system is observable, testable, measurable, and economically sustainable. Production means operating continuously under real workloads, evolving requirements, and growing organizational dependence — a considerably harder target, and one the industry is still working towards.

The next twelve months will be defined by agents becoming measurable: instrumented enough to improve, economical enough to scale, and reliable enough to depend upon. That is where the next phase of AI infrastructure will be built, and it’s the problem the Nebius Agents Blueprint is designed to solve.

Try it todayTry it today

The Blueprint is available today as open, runnable recipes from prototype to production-ready agent. The compliance agent is available as a complete end-to-end implementation you can clone and run in minutes.

• Start with Recipe 01
• Clone the compliance agent
• For enterprise teams that need a managed Blueprint solution, it is available to build through the TD SYNNEX partner ecosystem