Delegation Drift: When AI Helps Write Your Docs and Roadmap

Beyond the Prompt: AI and the Expanding Scope of Delegated Work

We asked the AI to document our options endpoint. It wrote the roadmap for our options exchange metadata — and helped us build a significantly better product for our clients.

TL;DR

We added a few features and were using AI to help generate documentation descriptions from our OpenAPI endpoint specs. It’s fast, consistent, and helps us stay lean. But recently, something unusual happened. When we asked Claude to write documentation for our options_v1_markets endpoint, it didn’t just document what was there — it documented what should be there.

The result? A dozen tickets for enhancements, roadmap discussions, and ideas that emerged from the AI’s output. This is a classic case of Delegation Drift: when AI overachieves its narrow task and ends up delegating work back to humans. In our case, that work led to substantial improvements in the quality, scope, and future value of the product. The AI's output ultimately shaped our roadmap for options exchange metadata and led to a more robust solution for our client base.

What Is Delegation Drift?

Delegation Drift describes a shift that occurs when AI tools go beyond their explicitly delegated task, creating new expectations or work downstream. It's not scope creep from stakeholders — it's scope expansion triggered by suggestion-rich AI outputs.

But here’s the thing: that drift isn’t a failure mode. In our experience, it was a feature. Delegation Drift helped us see beyond the limits of our current product and prompted improvements we hadn't scoped yet.

The Case: options_v1_markets Endpoint

This endpoint exposes static metadata about options markets: launch dates, integration flags, support contact info, mapping totals, and more. The schema was well-defined and reviewed. Claude was tasked with writing doc copy from the OpenAPI spec. Here’s what it inferred as “existing or expected” fields and concepts:

• Available Option Chains: Structured listings of tradable option instruments per market. Claude assumed these should be exposed to help consumers understand product coverage at a glance — including granularity of strikes, tenors, and active contracts. This metadata serves as a compact inventory of the market's instrument space. While we already capture this at the instrument level, at the market level it would be useful to provide summary indicators such as the number of listed underlyings, maximum and minimum tenor available, the depth of strike intervals per product type, or whether full chain coverage is consistent across expiries. Because this metadata is curated by our content team and rarely changes, a concise overview could support onboarding, strategy setup, and coverage validation for institutional users without requiring dynamic queries.
• Strike Price Intervals: The spacing between strikes in the options chain, critical for understanding market depth and liquidity provisioning. While we maintain detailed strike interval data at the instrument metadata level, Claude flagged the value of exposing general strike spacing patterns at the exchange level. This could include typical interval ranges (e.g., $1, $2.50, $5 strikes), whether intervals are uniform or adaptive based on underlying price, and how strike density varies across product types. This exchange-level perspective is essential for both retail UIs and institutional pricing models, where assumptions about overall market granularity directly affect slippage modeling and volatility surface construction.
• Expiration Schedules: Metadata around available expiration dates, recurrence patterns (daily, weekly, monthly), and how far into the future products are listed. This directly impacts the usability of the data for strategy backtesting, calendar spread modeling, and portfolio roll planning.
• Settlement Mechanisms: While we already capture this at the instrument level, Claude implicitly suggested surfacing common settlement characteristics at the market level — e.g., whether an options market supports cash settlement, physical delivery of underlyings, or synthetic mechanisms. These patterns often map to product classes like INVERSE, VANILLA, and QUANTO, depending on the combination of quote, base, and settlement currencies. Providing this summary at the market level helps consumers understand structural constraints without querying the full instrument list.
• Implied Volatility Calculations: Metadata indicating whether implied vols are available, how they are derived (e.g., mid-price models, Black-Scholes, skewed surfaces), and whether they are exchange-reported or independently computed.
• Exercise Styles (American/European): Describes which type of contract behavior each market supports, which has downstream implications for pricing, hedging, and early exercise modeling.
• Greeks Calculation Metadata: Whether the platform provides Greeks (delta, gamma, theta, etc.), how often they are updated, and what pricing assumptions are used — critical for margining and strategy simulation.
• Minimum Tick Sizes: While we already capture precise tick sizes at the instrument level, Claude suggested surfacing general tick size ranges and patterns at the exchange metadata level. This would provide consumers with quick insights into the overall precision characteristics of a market — such as minimum and maximum tick sizes across all option types, or whether the exchange uses uniform vs. variable tick schedules. This exchange-level summary helps with initial market evaluation, trading system configuration, and understanding general quoting constraints without requiring instrument-by-instrument analysis.
• Benchmark Scores (already present, but underutilized): AI emphasized their role in venue comparison, trading quality assessment, and market structure research. It recommended we surface them more clearly for quantitative and institutional users, helping teams prioritize venue selection and inform strategy allocation.
• Order Book and Trade Integration Flags (already present): These detail how real-time and historical data is ingested per exchange — including whether streaming or polling is used for trades and books. Claude proposed documenting them explicitly to help users gauge latency, completeness, and integration maturity.
• Margin Requirements and Collateral Specs (already included in internal data, but not highlighted): The AI assumed these were critical metadata points for any serious institutional platform. It recommended highlighting how option margining is handled (fixed, SPAN-based, etc.), whether collateral requirements differ across markets, and how margin offsets apply across multi-leg strategies.

Some of these were not part of the original documentation brief. All of them were plausible. Some of them now exist as tickets in our planning backlog.

What Changed?

This wasn’t our first time using AI to write documentation — we’ve used OpenAPI specs to drive automated doc generation for years. What was different this time was the depth of domain inference. Claude didn’t just reword the schema; it inferred what else should be in there if the goal was to serve real-world institutional options use cases.

The AI took a static spec and recontextualized it as a product brief. It connected dots across markets, assumed advanced use cases like volatility surface modeling and margin analytics, and suggested metadata enhancements accordingly. The result was not simply a better piece of documentation — it became the seed of a more complete and competitive options metadata platform.

Why It Matters

The implications of this Delegation Drift were immediately tangible. It transformed a tactical task — write documentation — into a strategic planning catalyst. The AI raised the bar for what “complete” metadata looked like, and did so based on pattern recognition across financial data architecture.

For our team, this meant creating new product tickets, adjusting our roadmap, and elevating our internal expectations of what our options market metadata should actually include. The suggestions were grounded enough to act on and visionary enough to change our direction.

Delegation Drift, far from being disruptive, pushed us toward excellence — and gave us the foundation to deliver higher value to our clients.

How To Work With Delegation Drift

To take advantage of Delegation Drift, you have to treat AI like a junior product strategist — not just a code assistant. This means reviewing its outputs not only for correctness, but for intent, assumption, and implied value.

You can operationalize this by separating speculative fields from confirmed ones, assigning reviewers for AI-driven insights, and ensuring those insights have a place in the backlog if they’re valuable. It’s also helpful to tune prompts or configure filters when you want strictly bounded behavior — but in our case, that unbounded scope was a gift.

Final Thoughts

Delegation Drift isn’t just a pattern — it’s a signal that your AI tools are capable of critical reasoning and synthesis. When used intentionally, this can drive clarity in product thinking, uncover gaps in schemas, and inspire iteration beyond your current roadmap.

In our case, what started as doc generation became cross-functional value creation. The documentation didn't just describe our product — it redefined it.

That’s the kind of drift we want more of.

Delegation Drift (n.) — The phenomenon where AI-generated output expands the scope of a delegated task, resulting in new human obligations or roadmap expansion beyond the original intent — often leading to better product outcomes.

‍