Open Models, Open Runtime, Open Harness - Building your own AI agent with LangChain and Nvidia

The agent architecture that powers Claude Code, OpenClaw, and similar systems isn't magic—it's a three-layer stack that's now fully replicable with open components. Nvidia and LangChain just released the pieces to build your own, and understanding this decomposition matters if you're running agents in production or trying to debug why your agentic system keeps failing on edge cases.

The stack breaks down cleanly: a reasoning model that generates actions, a runtime environment where those actions execute, and a harness that orchestrates the loop between them. Proprietary agents obscure these boundaries, making it nearly impossible to instrument individual components or swap out underperforming pieces. The open alternative—Nemotron 3 Super as the model, OpenShell as the runtime, and DeepAgents as the harness—gives you visibility into each layer and control over the failure modes.

Nemotron 3 Super is Nvidia's latest reasoning model, trained specifically for agentic workflows. It's not just a chat model with function calling bolted on—it's optimized for the action-observation loop that defines agent behavior. This matters because general-purpose LLMs struggle with the iterative reasoning required for multi-step tasks. You'll see this in your traces: models like GPT-4 or Claude often hallucinate tool outputs or lose context across turns when running as agents. Nemotron's architecture addresses this by maintaining better state coherence across the loop, though you'll still hit context limits on complex tasks beyond 15-20 steps.

OpenShell is where this gets interesting for production teams. It's a sandboxed execution environment that handles shell commands, file operations, and tool invocations with configurable security boundaries. Most agent runtimes are black boxes—you can't see what commands actually executed, what permissions were granted, or where failures occurred. OpenShell exposes these as structured logs, making it possible to trace exactly why an agent failed to complete a task. The security model is also explicit: you define allowed commands, filesystem access, and network permissions upfront rather than hoping the model doesn't generate something destructive.

DeepAgents is the orchestration harness that ties model and runtime together. It implements the standard ReAct loop—reason, act, observe—but with hooks for custom evaluators, retry logic, and fallback strategies. This is where you'll spend time tuning production behavior. The default configuration retries failed actions three times before escalating, but you'll want to adjust this based on your error distribution. If 80% of your failures are transient API timeouts, increase retries. If they're semantic errors like malformed tool calls, retries just burn tokens—fail fast and log for retraining instead.

The practical value here isn't just avoiding vendor lock-in, though that's real. It's having a reference implementation you can instrument, fork, and modify. When your agent starts hallucinating tool outputs or gets stuck in loops, you can trace through the harness code, inspect the exact prompts sent to the model, and see what the runtime actually returned. With proprietary systems, you're guessing based on API responses and hoping the vendor's logging is sufficient.

The switching cost from a proprietary agent to this stack depends on how much custom logic you've embedded in vendor-specific APIs. If you're using Claude's artifacts or OpenAI's structured outputs heavily, expect a week of porting work. The model quality gap is narrowing—Nemotron 3 Super is competitive with GPT-4 on agentic benchmarks like SWE-bench—but you'll need to retune your prompts and evaluate on your specific tasks before committing.