Answer a few high-level questions about your coding-agent workflow. The Memory Reliability Lab scores where your team is losing lessons across tools, repeating human corrections, relying on stale context, or missing the governance needed for production agents.
Why this matters now
Teams are using Claude Code, Cursor, Codex, Copilot, Windsurf, internal agents, and local model stacks. The learning is fragmented. One tool remembers something another never sees. Human corrections happen in PRs and vanish. Context files grow stale. Agent traces show what happened, not what should survive.
The first run proves an agent can work. The second run proves whether the team can learn.
What it measures
Every answer scores against six signals of production-memory health. Together they form a Memory Reliability Score, a verdict band, and a confidence rating.
Are lessons from agent work retained and reused — or do agents keep relearning what your team already knows?
Are instructions stale, generic, duplicated, or over-broad? Static files do not know when they should be retired.
Is there a clear path from work → correction → memory → future retrieval — or do lessons disappear into chat?
Can old or conflicting guidance mislead future agents? After change, does anyone retire what should not survive?
Can teams prove where guidance came from, who approved it, who can use it, and whether the agent retrieved it?
How much of the score is based on concrete workflow facts vs. unknowns? The judge tells you what it could not see.
Privacy guarantee
The public diagnostic stores only high-level answers and a score. Private deep-dive audits happen later, under your security process.
Sample report
Every diagnostic ends with a score, a band, a plain-English verdict, ranked failure modes, a learning-leak map, and a recommended first pilot.
The main issue is not lack of context. It is that context is static, tool-specific, and disconnected from outcomes. Agents are likely repeating corrections your team has already paid to make. The fastest improvement is not more documentation — it is a write path that turns recurring corrections into scoped, trusted memory before the next agent picks up the task.
Claude, Cursor, Codex, and Copilot do not share a durable memory layer.
Useful review feedback appears in PRs or chat but is not promoted into scoped memory.
Old guidance survives after code, policies, or architecture change.
Choose one repeated workflow where agents frequently need review. Capture ten recurring corrections as candidate memories. Each carries scope, provenance, owner, trust signal, and a retirement path.
The activation metric: the first reused memory that improves a later task.
Failure modes it detects
The judge cross-references your answers against these patterns. It does not need to read your code to know which ones are showing up.
Every run starts like the repo is new — token cost recurs forever.
AGENTS.md / CLAUDE.md becomes broad, stale, and expensive.
Claude, Cursor, Codex, and Copilot each learn separately.
The same review comment appears again next week.
Agents consume context, but nothing they learn becomes durable.
Old or over-broad guidance misleads later agents after change.
Nobody knows why a rule or memory is trusted.
Useful memory cannot safely scale across teams.
The team cannot prove memory improved a later run.
How Memco fixes the loop
Memco is the layer between agent runs. It turns repeated corrections into scoped candidate memories, attaches provenance and ownership, retrieves them before the next relevant task, and retires what stops working. The model can change. The memory should not.
Run the diagnostic
Seven minutes. Six dimensions. A rubric-based AI judge. No code, no traces, no logs. Just the patterns you already know.
Pattern-level answers only. No private code required.