Transforming Static Outputs into Dynamic High-Value Contexts
We are drowning in brilliant, often useless noise.
In our pursuit of artificial general intelligence, we’ve successfully developed engines that produce breathtaking insights. Tools like Claude, Grok, and their ilk can synthesize, analyze, and sometimes hallucinate at superhuman speeds. They hand us novel strategies, deep-cut data correlations, and entire reports on quantum ethics.
And then they run away, like they’re distracted by the fresh tray of finger foods at a dinner party.
What we receive is a data dump, a read-only fortress. It’s elegant, static, and fundamentally dead on arrival. This is the great, unspoken “blind spot” of the generative AI revolution: the outputs are not the beginning of a workflow; they are the end of one. And only the end, because memory features only make it worse.
This isn’t just a usability problem. It’s an evaluation problem. The AI industry is “eval-washed,” obsessed with pre-launch benchmarks that test an AI’s ability to take a test. They are decoupled from reality. The real world is not a multiple-choice question.
The only evaluation that matters is the one performed by a domain expert, in situ, at the moment of need. But our current tools make this impossible. We, in our frustratingly human way, don’t just want a static answer. We want to evaluate it. We want to argue with it, highlight its half-baked references, correct its stale data, and inject our own expert context.
But we can’t. Our tools force us into a copy-paste exile, fracturing our flow. This act of “context-breaking” doesn’t just kill our productivity; it prevents the most valuable form of AI feedback from ever happening.
The Static Trap is an Eval Trap
Let’s dissect the frustration. Dr. Elena Vasquez, a climate researcher, asks an AI to generate a report on urban heat islands. Grok delivers a 1,000-word monolith.
It’s good. But it’s also incomplete and wrong.
It confidently cites 2023 studies. Elena, who just read the 2025 IPCC addendum, knows this data is obsolete. She sees that the AI overlooked the staggering cost inequalities of rolling out new nanomaterials in the Global South.
In the current paradigm, Dr. Vasquez is just a “user.” She is a passive consumer of a flawed artifact. But in reality, Dr. Vasquez is the most qualified evaluator on the planet for this specific output.
She has two “bad” options:
- Ignore the output, reinforcing the “AI is a toy” narrative.
- Become an unpaid QA intern. Manually copy, paste, and fix the work, with 100% of her expert evaluation vanishing into a private Word doc, never to benefit the system.
This is the static trap. An AI response that cannot be evaluated, corrected, and expanded in place is not just a broken product. It’s a failed evaluation model.
I’ve written extensively about evaluations. I even tried to land a job at Superhuman (pre-rebrand), but HR wasn’t familiar with any urgency concerning in-situ evals. 
The Environment for Evolution
To fix this, we need a new methodology, not just a new tool. We need to stop thinking about “outputs” and start thinking about “living scaffolds.” We need an environment where the workflow itself becomes the evaluation.
This is where the synthesis of platforms like Superhuman (for linguistic overlay) and Coda (for structural, data-driven architecture) becomes the stage. But they are only the stage.
The hero is the agent that lives between them. It doesn’t [yet] exist, but I’ve named it Cascade, and it’s the perfect description. Cascade is the active, intelligent (agentic) force that turns a static artifact into an engine for in situ evaluation.
The Agent is the Eval: Cascade in Action
Let’s rerun Dr. Vasquez’s scenario, this time with Cascade. It’s not just a workflow tool; it is the mechanism for her expert evaluation.
1. Ingestion and the In Situ Eval:
Elena gets the Grok report. Cascade, running as a browser extension, overlays a minimalist toolbar. She highlights the stale 2023 data: “Reflective coatings can reduce surface temperatures by 10-15°C.”
- Cascade Action: This highlight is not just a highlight. It is a logged, in situ evaluation. Cascade recognizes the stale data. A small prompt appears: “Factual evolution detected. Cascade a rewrite?” Elena clicks. Cascade queries its own knowledge graph and generates an inline diff.
[REF-23: Tech Update (Eval: Stale Data)]
Original: …reduce by 10-15°C (2023 studies).
Cascade v1.1: Reflective nanomaterial coatings now achieve 20°C reductions (IPCC 2025 Addendum). [Source: DOI:10.1038/s41586-025-00123-4].
The expert (Elena) has, with one click, performed a high-fidelity evaluation that is now part of the document. The context is current, and the reason for the change is captured.
2. Expansion as Expert Scaffolding:
Elena isn’t just correcting; she’s expanding. This is the second, deeper form of in situ eval. She right-clicks the new reference: “Cascade: Infuse Equity Perspective.”
- Cascade Action: Leveraging Superhuman’s voice modeling (trained on Elena’s own work), the agent appends a layered footnote. It doesn’t just add data; it adds her expert analysis.
[EXPANSION-23A: Equity (Eval: Missing Context)]
Nanomaterial efficacy is high, but adoption in low-income megacities is blocked by a 30% cost premium. Pro Tip (from your notes): Tie this to carbon subsidy models.
Seed for Cascade Query: “Simulate equity-ROI for green roofs vs. nanomaterials in Mumbai, 2030.”
The AI output has become a scaffold for her expert-driven inquiry. The “eval” is no longer a simple “pass/fail” but a rich, generative act of co-creation.
3. The Cascade Vault: An Auditable Log of Evals
Elena tags two more items. Cascade assembles these annotations into a “Cascade Vault.” This is not just a bundle; it is an auditable, expert-driven evaluation log.
- Cascade Action: The agent previews the bundle: “Cascade into Grok next?” Elena hits ‘Go.’ Cascade auto-pastes the entire thread as a primed prompt.
“Evolve the heat island report using these expert annotations: Correct [REF-23] for factual staleness, infuse the missing equity context from [EXPANSION-23A], and use this expert-seeded query to model the Mumbai ROI.”
From Static Artifact to Living Evaluation
The next AI doesn’t just get a new prompt; it gets a lesson. It receives the full context of a domain expert’s in situ evaluation, complete with the “why” and “how” of the corrections.
This is the loop. This is how we fix the blind spot. This is context engineering.
Cascade, therefore, is not just a workflow tool. It is an epistemic tool. It’s the linchpin that turns passive consumers into active evaluators. It’s the only way to bridge the gap between the sterile, abstract world of benchmarks and the messy, vital world of real-world expertise.
We have to stop building tools that dump static artifacts. It’s time to build agents that respect the expert’s evaluation. The story of AI isn’t written in isolation; it’s etched in these layers of in situ correction. By harnessing an agent like Cascade, we don’t just patch the gap.
We finally integrate the evaluation into the workflow itself.