Building Reliable AI Agents Requires Compiler Like Systems
At TensorStax, we’re building autonomous agents that operate in one of the most unforgiving domains out there, data engineering. And reliability isn’t optional here. A hallucinated schema, a broken DAG, or a failed job silently corrupting data? It can break full prod environments.
What we’ve learned is that the problem isn’t just with the models themselves. It’s about how they interface with the real world. And the most effective thing we’ve built to solve that is something we call the LLM Compiler.
Why Agentic Systems Break So Easily
First, deep learning models do generalize well on out of distribution tasks. Most enterprise workflows are highly specific and full of edge cases that rarely show up in training data. I talk about this
Second, long horizon tasks like generating full pipelines or orchestrating multi-step jobs tend to suffer from compounding token level errors. One incorrect assumption early in the task propegates an error across the whole chain of actions. And suddenly, the agent’s state is completely misaligned from what it thinks it’s doing.
And finally, most software systems weren’t built for machines. They were designed for humans, full of implicit assumptions, verbose configurations, and context scattered across docs, UI, and code. LLMs struggle to maintain and reason over that kind of fragmented, non-standardized input.
An example I usually give is: we don’t code in binary, even though computers understand binary. We use high level programming languages because they abstract away complexity and let us express intent clearly. I think LLMs need that same kind of abstraction to interact reliably with the external world.
What Is an LLM Compiler?
Its a layer which sits between the agent and the execution environment. Instead of generating raw tool specific code or manually scripting every step through a CLI or Python, the agent produces a structured intermediate config, a normalized spec, inputs, dependencies, schedules, and configuration.
The compiler then translates that config into a concrete set of deterministic steps for the appropriate system, whether that’s a workflow engine, query processor, orchestration layer, or custom API. It handles boilerplate, enforces conventions, resolves references, and validates against the underlying infrastructure.
Just as critically, it provides clean, interpretable feedback. Did the task execute? Did the operation succeed? Were all checks and validations passed? This feedback isn’t probabilistic or inferred, it’s deterministic. And that’s what makes the system both trainable and reliable.
Example for running a batch job on AWS:
By removing as many unnecessary variables and constraining generations we achieve a few benefits:
- More determinism in outputs given constrained generation for
- Minimized potential for errors to propegate given immediate feedback from compiler
- Deterministic signal for reinforcement learning
The Compiler as a Training Environment
Once you introduce reinforcement learning, the benefits compound.
Because the compiler layer tracks compilation success, runtime errors, schema mismatches, and test results, it naturally generates clean reward signals. These aren’t synthetic scores or heuristics, they’re real world signals from real-world systems.
This gives us a reliable environment to train specialized models that understand how to plan accurately and reason with structure. And because the compiler handles execution, the model’s learning loop becomes significantly more stable.
We’re now fine-tuning task-specific agents for each tool dbt, Airflow, Spark using this exact architecture. And we’re seeing performance scale with each iteration.
Why This Works So Well for Data Engineering
Data engineering is rigid and highstakes. Unlike general purpose coding where many implementations work, data workflows often have only one valid form. A small error in a SQL transformation or DAG setup can silently corrupt data or break downstream systems.
This is where the compiler makes a difference.
It enforces structure by requiring agents to output normalized configs. This narrows the reasoning/action space and makes it easier to learn valid behaviors. It also provides fast, deterministic feedback. The compiler validates inputs before execution, catching issues early and giving agents clear signals to improve.
It works across stacks. Whether you use dbt, Airflow, or Spark, the agent generates an abstract spec, and the compiler maps it to the underlying system. This keeps things consistent and production-safe.
The feedback is real. Every validation, failure, schema mismatch, or test result becomes a reliable training signal. This creates a strong foundation for reinforcement learning.
And most importantly, it keeps execution safe. You can gate actions, enforce policies, and control how agents interact with production systems.
What’s Next: Agent Reliability Infrastructure
Alongside the compiler, we’ve been building a suite of internal tools to make agents easier to evaluate and trust. We just released AgentTrace, a lightweight tracing and evaluation framework that gives you a local dashboard, tagging, and scoring interface. We use it daily to monitor how our agents behave.
We’re also preparing to open source:
- A routing layer to dynamically select between multiple models and sub-agents
- A synthetic data generation toolkit for stress testing across edge cases
- And a reinforcement learning environment builder designed specifically for structured task spaces
All of it built to make agents not just smarter, but actually usable in production.
We believe the future of agents starts with better systems outside of the models. And for us, the LLM Compiler is how we’re building that.