Developer trust gap
AI coding tools are widely used, but trust in their output remains limited. That points to a need for better review, validation, and evidence.
AI adoptionlimited trustreview needevidence
Local-first · Runs beside your coding agent
The Context Layer for AI Coding
Local-first context, validation, and evidence for LLM-powered coding workflows.
Your agent writes the change. Reality Graph states what the change was supposed to be, runs the checks itself, and reads the evidence out of Git.
Public claims only when they are earned. Every limit is written next to the capability it limits.
See it run
Thirty seconds, from a sentence to a verdict.
Scattered context resolving into a structured map, then into evidence you can point at. It plays from this site: no external player, no cookies, no sign-up.
The problem
AI coding tools are powerful. The workflow around them still breaks.
Claude Code, Cursor, Copilot, and Antigravity raised the ceiling on what a model can do. But the loop around them tends to break in the same places, in the same order:
- 01
The task gets vague
A request that's clear in your head reaches the model underspecified. The goals, constraints, and done-criteria stay implicit.
- 02
Context gets noisy
The wrong files, stale logs, and half-related history crowd out the few things that actually matter.
- 03
Output looks plausible
The result reads convincingly, which makes it harder to notice what's quietly wrong.
- 04
Validation is scattered
Tests, risks, and acceptance criteria live in different places, so nothing checks the change as a whole.
- 05
Review takes too long
Reviewers rebuild intent from the diff alone, paying again for context that was never written down.
- 06
Evidence is missing
When something breaks later, there's no record of what changed, what was checked, or why it shipped.
Where a control layer steps in
- prepare context
- keep boundaries visible
- preserve validation
- record evidence
A high-level shape, not a feature list. A human stays in control throughout.
The thesis
Stronger models are not the whole answer.
AI coding also needs a better operating layer. One that understands the task, keeps boundaries visible, prepares focused context, preserves validation, and records evidence.
What Reality Graph adds
Four things happen around every run. None of them is writing your code.
Reality Graph does not replace your AI coding tool. It sets up the run around it: the task, context, memory, validation, and evidence. That way the human can review what happened with more confidence.
Mission Contract
Keeps the goal, scope, constraints, and done criteria visible before the model starts working.
Read moreContext Packs
Prepares focused task context instead of pushing vague prompts, repo noise, and scattered history into the run.
Read moreCodeGraph
Brings back useful project rules, decisions, and repeated lessons when they are relevant to the current task.
Read moreValidation Receipts
Connects the task to tests, risks, and review criteria before the output is trusted.
Read moreTrust Review
Makes it easier to see what changed, what was checked, and what still needs human review.
Read moreApprovals
Keeps Reality Graph advisory by default. Risky actions stay visible instead of becoming invisible automation.
Read moreHow it works
- 1Start with a task
- 2Reality Graph prepares the run
- 3Your AI coding tool works
- 4You review validation and evidence
The AI coding tool still does the coding. Reality Graph keeps the surrounding workflow structured, reviewable, and human-controlled.
Go deeper
Four pages, in the order most people need them
Each one answers a different objection, and none of them needs you to talk to anyone first.
How it works
One run from contract to verdict, in six steps, with the two steps that are not ours marked as such.
OpenWhat is built
The capability list with a third column: the stated limit of each one, and what is not built at all.
OpenDoes it fit my tools
One tool has a real adapter. The rest work beside it through a pasted prompt, and this page says which is which.
OpenWhere my code goes
The complete first-party data path, including the two cases where something does leave the machine.
OpenWant to see what your last agent run would have looked like?
Request accessThe need
Why AI coding still needs a control layer
Illustrative workflows and external signals, not Reality Graph performance claims.
Context chaos
A messy prompt, repo noise, terminal logs, and a vague task rarely add up to focused context. Illustrative, not a numeric claim.
promptrepo noiseterminal logsvague taskfocused context
Evidence gap
The hard part is not only generating code. It is knowing what happened across diffs, tests, and risks, and whether it is safe to ship.
AI outputgit difftestsriskshuman review
Stack Overflow Developer Survey 2025 reports that more developers distrust AI tool accuracy than trust it, with only a small share highly trusting outputs. Source: Stack Overflow Developer Survey 2025
Where a control layer helps
Illustrative, not a Reality Graph performance claim.
Bring the few things that matter into focus and leave the noise out. Instead of pushing a whole repo, stale logs, and a vague prompt into the run, a control layer prepares a tight, task-shaped slice of context before the model starts.
- Pull in the files, rules, and history that relate to the task
- Keep out repo noise, dead code, and unrelated logs
- Carry the goal and constraints alongside the context
Keep risks, tests, and acceptance criteria visible while the work happens, not after the fact. A control layer ties the change back to how it will be checked, so output that only looks plausible isn't mistaken for output that's verified.
- Connect the task to the tests and checks that cover it
- Surface risky areas before the output is trusted
- Make acceptance criteria explicit, not implicit
Capture what changed and why, so a run can be reviewed and understood later. A control layer keeps a trail across diffs, tests, and decisions, instead of leaving reviewers to rebuild intent from the diff alone.
- Record what changed and what was checked
- Keep the reasoning attached to the result
- Make a run reviewable long after it finishes
Keep a human in the loop: advisory by default, with no autonomous commits. A control layer makes risky actions visible and easy to approve or reject, instead of turning them into invisible automation.
- Stay advisory unless a human approves
- Make risky steps visible at decision points
- Leave the final call with the developer
External market and problem signals, not Reality Graph performance claims. Source: Stack Overflow Developer Survey 2025
The cost
What unverified AI code is costing you
Three questions, then arithmetic you can check. The model prices a problem, not a product, and every assumption is labelled and replaceable.
People, not seats
1100+
A month, from your Git or PR data
560
Share of merges that are AI-assisted
A rough estimate is enough
The figure your finance team uses
€30€250
Every line above is a control. Move one and the estimate recomputes, which is the honest substitute for a range.
Estimated cost of verification debt
Example – illustrative arithmetic, not a benchmarkEstimate updated.
€67,000
per year · €5,580 per month
74.4 hours a month, 60 of them spent working out what a change was meant to do.
Modelled on about 120 AI-assisted merges a month.
- Review reconstruction€4,500 · per month
- Working out what a change was meant to do before you can judge it. This line is in nobody's budget: it is paid on every AI-assisted change, not only the broken ones, and it is four fifths of the total above.
- Rework on churned code€1,080 · per month
- Changes reworked for a defect within two weeks of merging. The 2 % is illustrative: replace it with the rate your own tickets show.
- Incident allowance€0 · per year
- None is assumed. One belongs here only once you have your own incident class, frequency and expected loss, so the model leaves it to you rather than inventing it.
What the model assumed for you
- 0.5 hours of review reconstruction per AI-assisted change
- 2 % of AI-assisted changes reworked for a defect within 14 days, an illustrative rate to replace with your own
- 6 hours to rework one churned change
Which line a verification practice is designed to address
The reconstruction line shrinks when a change arrives with its task, its checks and its evidence attached: the reviewer stops reconstructing intent and starts judging it. Reality Graph is designed for that. No before-and-after measurement of it exists yet, so no percentage is offered here.
Teams that verify systematically report 44 % fewer outages caused by AI-generated code. Sonar, State of Code 2026
Where this stops paying
Below roughly 30 to 40 AI-assisted changes a month the example is marginal. Volume decides this, not headcount: two people running agents all day are past it, and twenty people barely using AI are not. For a throwaway prototype the answer is no.
One run, one contract, one verdict you can point at.
Tell me which coding tools you run and what keeps breaking in review, and you get a straight answer about fit.
What Reality Graph is
A context, validation, and evidence layer around the tools you already use.
Focused context
Prepares focused context for the AI coding tools you already use.
Visible validation
Keeps risks, tests, and acceptance criteria visible.
Evidence trail
Makes AI-assisted runs easier to review and understand.
Local-first boundary
Built around local workflows and code privacy.
Human control
Advisory by default. No autonomous commits.
Availability
Early access opens soon.
Reality Graph is in closed beta and opens to a small early-access group next. Access is a conversation about fit rather than an automatic activation, and the plans on the pricing page open in turn.
Closed beta todayEarly access openingPlans open in turn
Who should join
Built for whoever gets asked what changed.
AI-coding power users
For developers who use AI coding tools daily and keep fighting context, validation, or review friction.
Technical reviewers
For people who need to understand what changed, why it changed, and whether it is safe to trust.
Small technical teams
For teams exploring AI-assisted development but unwilling to give up control, evidence, or local-first boundaries.
By design
The boundaries are the product, not the fine print.
Reality Graph is designed as a control layer for AI coding workflows, not another autonomous coding agent. It keeps context, validation, and evidence visible while humans stay in control.
- Works with existing AI coding tools
- Human-controlled by default
- No launch claims or fake benchmarks
Articles
Go deeper: verification, explained
Cited, dated articles on verifying AI-generated code - the concepts behind Reality Graph, useful with or without it.
FAQ
Questions, answered plainly.
- What is Reality Graph?
- Reality Graph is a local-first context, validation, and evidence layer for LLM-powered coding workflows. It works alongside the AI coding tools you already use to keep context focused, validation visible, and evidence easy to review.
- What does Reality Graph add?
- It adds a control layer around AI coding runs: task boundaries, focused context, project memory, validation awareness, and an evidence trail. The AI coding tool still does the coding; Reality Graph keeps the workflow easier to review.
- Is Reality Graph an AI coding agent?
- No. Reality Graph is a control layer, not an autonomous coding agent. It is advisory by default and does not write or commit code on its own, so you stay in control.
- Which tools is Reality Graph designed to work with?
- It is designed to work with existing AI coding tools and local development workflows, rather than replacing them.
- Is Reality Graph available now?
- Not yet. Reality Graph is in closed beta and early access opens to a small group next. It is a conversation about fit rather than an automatic activation.
- How do I get access?
- Request access, or reach out to the founder directly. I'm looking for a small number of technical users and teams who feel this problem deeply.
Founder note
“I'm building Reality Graph because I kept hitting the same wall: the model was powerful, but the workflow around context, validation, and evidence was too fragile to trust.”
Request access
Want to test it when it is ready?
Tell me how you work. I'm looking for a small number of technical users and teams who feel this problem deeply and will tell me the truth about it.