When we launched Drane Labs, one of our foundational commitments was to transparency in how we build AI systems. As someone who spent years working on distributed training infrastructure at scale, I've seen firsthand how opacity in development processes creates technical debt, reproducibility crises, and erosion of stakeholder trust. This post outlines our approach to transparent AI development and the specific frameworks we use to maintain it.

The Reproducibility Problem

The AI research community faces a reproducibility crisis. A 2024 study found that fewer than 30% of published machine learning papers provide sufficient information to reproduce their core results. This isn't simply an academic concern—it affects production systems, safety evaluations, and our ability to build on prior work effectively.

At the infrastructure level, reproducibility requires more than sharing code. It demands careful versioning of datasets, dependency management, hardware specifications, random seed control, and comprehensive logging of hyperparameters. When training runs cost thousands of dollars in compute, non-reproducible results represent both wasted resources and lost institutional knowledge.

We approach this through what we call "training provenance tracking"—every model we train maintains a complete lineage record including data snapshots, commit hashes, environment specifications, and intermediate checkpoint metrics. This isn't just documentation; it's part of our training orchestration system. If we need to reproduce a result from six months ago, we can reconstruct the exact environment that produced it.

Evaluation Frameworks That Matter

Transparency in evaluation is distinct from transparency in training. A model might be trained with perfect reproducibility but evaluated using metrics that obscure its actual behavior. This is where evaluation frameworks become critical.

We separate our evaluation into three layers: capability benchmarks, behavioral assessments, and deployment monitoring. Capability benchmarks measure what a model can do in controlled conditions—standard academic datasets, reasoning tasks, domain-specific tests. These establish baseline performance but tell us relatively little about real-world behavior.

Behavioral assessments examine how models respond to distribution shift, adversarial inputs, edge cases, and prompt variations. This is where we discover failure modes that don't appear in clean benchmark data. We maintain a continuously growing test suite of challenging examples, many derived from production incidents or red-teaming exercises.

Deployment monitoring tracks model behavior in actual use. This includes standard metrics like latency and error rates, but also subtler signals: how often users retry failed requests, what kinds of follow-up questions appear, where conversations terminate prematurely. These patterns reveal issues that synthetic evaluation misses.

The key principle is that all three evaluation layers must be documented and accessible to stakeholders. When we report model performance, we're not cherry-picking the most favorable benchmark. We're showing the full picture: where the model excels, where it struggles, and what unknowns remain.

Open Process Doesn't Mean Open Weights

There's often confusion between process transparency and model transparency. Some argue that truly transparent AI development requires releasing model weights publicly. While we support open research and have released several smaller models under permissive licenses, we don't believe weight release is the only path to transparency—or even always the right one.

Process transparency means stakeholders can understand how decisions are made, what tradeoffs are considered, and how systems are evaluated. It means our safety testing procedures are documented, our data sourcing is auditable, and our deployment criteria are explicit. Someone reviewing our work can assess whether our methodology is sound without necessarily having access to production model weights.

This matters particularly for systems that handle sensitive data or operate in security-critical contexts. A research model trained on public datasets might be appropriate for open release. A production model fine-tuned on proprietary data or deployed in a high-stakes environment requires different considerations. The transparency obligation remains, but it's fulfilled through documentation, third-party audits, and structured access rather than unrestricted distribution.

Documentation as Infrastructure

One pattern we've adopted from traditional software engineering is treating documentation as first-class infrastructure. Our internal "model cards" are generated automatically from training metadata and stored alongside model artifacts. They include data provenance, performance breakdowns across demographic groups, known failure modes, and recommended use cases.

These aren't marketing documents—they're technical specifications. When an engineer considers using a model for a new application, the model card provides the information needed to assess fit. Does this model perform adequately on the relevant distribution? Are there known biases that would affect this use case? What monitoring should be in place?

Similarly, our dataset documentation follows a structured template covering collection methodology, consent frameworks, filtering decisions, statistical properties, and known limitations. These documents are versioned alongside the datasets themselves. If we make a change to our data processing pipeline, the documentation reflects that change with the same version control discipline we apply to code.

This approach scales. As our model catalog grows, documentation systems prevent institutional knowledge from living exclusively in individual engineers' heads. New team members can understand why decisions were made. External auditors can assess our processes systematically.

The Cost of Opacity

I want to be direct about why this matters beyond principle. Opacity has concrete costs.

When training processes aren't reproducible, debugging becomes archaeology. An engineer investigating a performance regression might spend days trying to reconstruct what changed between model versions. With proper provenance tracking, this becomes a database query.

When evaluation frameworks aren't comprehensive, deployment surprises are common. A model that performs well on standard benchmarks encounters unexpected failure modes in production. If behavioral assessment and red-teaming had been thorough, many of these issues would surface before deployment.

When documentation is incomplete or out of sync with reality, technical debt accumulates rapidly. Engineers make assumptions based on outdated information. Integration issues multiply. The system becomes brittle and difficult to modify safely.

Transparency isn't altruism—it's engineering discipline that pays dividends in system reliability, team velocity, and stakeholder confidence.

Practical Implementation

Our implementation of these principles is iterative and imperfect. Some specific practices we've found valuable:

Automated logging infrastructure: Training and evaluation runs emit structured logs that feed into a queryable database. This happens automatically—engineers don't manually write evaluation reports.

Checkpoint analysis tools: We maintain tooling for analyzing model checkpoints—not just final performance, but training dynamics, convergence patterns, and intermediate evaluations. This helps us understand what changed during training, not just what the final result was.

Public evaluation datasets: Where possible, we include public benchmark datasets in our evaluation suite. This allows external researchers to replicate at least some of our assessments without access to our internal systems.

Red-team reports: Our internal red-teaming efforts produce detailed reports that become part of the model documentation. These describe discovered failure modes, attack vectors, and mitigation approaches.

Third-party audits: For high-stakes deployments, we engage external auditors to review our evaluation methodology and verify documentation accuracy. These audit reports are shared with clients and, where appropriate, published.

Looking Forward

Transparency in AI development is not a solved problem. As systems become more complex—agents with memory, multi-modal models, systems with tool use—our evaluation and documentation frameworks must evolve.

One area we're actively developing is evaluation of agent systems. Traditional model evaluation assumes stateless input-output behavior. Agents maintain context over time, take actions in environments, and compose multiple capabilities dynamically. How do we evaluate such systems comprehensively? How do we document their behavior in a way that captures both capability and risk?

Another challenge is scaling transparency practices as teams grow. When Drane Labs was five people, informal communication sufficed. As we scale, we need systems that maintain transparency without creating unsustainable documentation overhead. This is an ongoing design problem.

The AI field moves quickly. Transparency practices that work today may be inadequate tomorrow. But the underlying principle remains constant: building trustworthy AI systems requires making development processes, evaluation criteria, and system limitations accessible to those who need to understand them. That's not a luxury or an afterthought—it's foundational engineering practice.

Annabelle Cortez is Chief Technology Officer at Drane Labs, where she leads infrastructure and training systems development.