AI coding agents are choosing which developer tools win.
We help developer tools become agent-native: selected and implemented by coding agents without friction.
Quantify, diagnose, and win your machine market share.
Your next thousand users are agents. See where they drop you. Fix it before you ship.
See exactly why agents drop you
Map the competitive terrain. Locate structural leaks. Receive gate-by-gate trace data pinpointing the precise APIs or documentation clusters triggering agent drop-off.
Prove the fix before it ships
Test SDK, API, and doc changes against a digital twin of your integration surface before anything ships. A fresh agent cohort runs the same tasks against the candidate change; you see whether agent conversion improves before the change touches production.
You are native to agent implementation
After you ship, we re-measure. Can the agent install, authenticate, make the first call, and keep it working at the rate we predicted? Realized gain is compared against predicted gain. When an agent reaches for you, it gets all the way through.
The agent does not always pick a vendor.
Agent-Native Market Share
Market share in the agent era is a distribution across prompt slices. The same category, asked with a different constraint, produces a different winner. You can be the reflexive default in the generic prompt and absent from the repo one constraint later.
Add password reset emails to this Next.js app.
No vendor is named in the prompt. The bars are the share of runs in which each option ends up wired in the code. In one task you are the default. In the next you are not in the repo at all. Illustrative values, not published measurements.
NativeFold varies each task across the dimensions that define real demand: price ceilings, region and compliance requirements, frameworks, languages, feature constraints. Measured by running real coding agents on real tasks, not by asking them what they prefer.
When the agent reaches for a generic library or just writes the code itself, that share belongs to nobody yet.
Shaded bands are Wilson intervals, always on. Each release is re-run paired against the outgoing model, so a step in the line is attributable rather than guessed. The board shows who agents install. Illustrative values, not published measurements.
Mentioned is not integrated
We measure what agents do, not what chatbots say.
A wave of AI-visibility tools counts how often a brand gets cited in a chat answer to a human. That is a different surface, a different decision, and a different fix.
Are you mentioned?
Scores brand presence in generated answers. The remedy is a marketing motion: publish more content, land in more listicles. Nothing about whether the tool survives contact with a real build.
Are you installed, called, and kept?
Records what an agent installs, wires, calls, and retains in a sealed, instrumented build, over many runs. The remedy is an engineering motion: the env var agents guess wrong, the error string that isn't machine-readable, the missing endpoint that triggers a workaround or a substitution.
The evaluation funnel
An agent can drop you at seven points.
Each one implies a different fix. No complaint, no support ticket, no warning.
NativeFold scores every run through each gate, so you learn not just your position but the exact moment adoption breaks.
Empirical rigor
Find where agents drop you. Fix what makes them switch.
NativeFold runs real coding agents on real tasks and measures whether they find, choose, install, and run your product, pick a competitor, or just build it themselves. Then it tests which changes move the decision.
Statistical saturation
Selection is a distribution, not a single pick. We run simultaneous task variations with fresh states to eliminate run-to-run stochastic variance and establish tight confidence intervals.
Pinned orchestration
Every model family, system prompt, and package dependency is fully hashed for exact reproducibility.
Raw traces
Every prompt, downstream tool call, and environmental error response is preserved for microscopic forensic audit.
Surface differential
Deploy an experimental SDK version or doc structure, rerun the task cohort, and isolate the exact conversion delta.
Every data point is derived from isolated execution environments running localized, pristine agent instances under strict variable controls.
From the blog
Field notes on how agents choose.
What we learn running real coding agents through real builds, and what it means for the shape of your product.
Why now
The internet is changing its primary consumer. We are building its new optimisation layer.
More and more software is discovered, chosen, installed, and run by coding agents, not people. The funnels built for human attention cannot see that traffic and cannot move it.
Why us
Agentic evaluation should be treated as experimental science, not leaderboard competition.
Benchmarking coding agents through simulation requires a discipline that fields such as aerospace, aviation, and nuclear power have spent decades developing*: reliability must be quantified and treated as multidimensional and explicit.
At NativeFold, we apply the same approach to coding agents. We run simultaneous task variations from a fresh state to control stochastic variance, observe how agents select and implement software, and turn behavioral evidence into concrete changes to SDKs, APIs, documentation, and installation paths. This prevents a common failure in agent benchmarking: mistaking noise for signal and deriving misleading product recommendations from it.
* Rabanser, S., Kapoor, S., Kirgis, N., Liu, Y., Utpala, S., and Narayanan, A. “Towards a Science of AI Agent Reliability.” International Conference on Machine Learning, 2026. Princeton University.
The name
A protein's native fold is the precise three-dimensional geometry that allows it to bind, interact, and function within a cellular system. If the configuration is misaligned by a fraction of a nanometer, the biological utility drops to zero. Software architecture behaves identically in an agentic ecosystem. A tool can possess market-leading features, yet remain completely invisible if its public interface contradicts the consumption patterns of language models. NativeFold exists to measure this new environment. We provide the empirical evidence, the run traces, and continuous optimisation layer required to reshape your product for the way machines now decide.