Governed continual learning

Everyone hasthe latest models.

Your advantage is the experience they don’t have.

memco helps turn your team’s hard-won know-how into private, shared learning for your agents. They can use it, improve it and keep it as models change.

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The current enterprise cohort is full · Individuals and startups sign up for free

Trusted by engineers at

  • Microsoft
  • Stack Overflow
  • CodeRabbit

Works with your agent stackModel-agnostic · IDE-agnostic · harness-agnostic · tool-agnostic

Add to ClaudeAdd to Codex
  • Models
    • Claude
    • OpenAI
    • Gemini
    • Llama
    • DeepSeek
    • Qwen
  • IDEs
    • Cursor
    • Windsurf
    • Zed
    • JetBrains
    • GitHub
    • VS Code
  • Harnesses
    • Claude Code
    • Codex
    • Copilot
    • Meta Muse
    • ChatGPT Dots
  • Tools
    • Linear
    • Jira
    • Confluence
    • Notion
    • Zendesk
    • Sentry
    • PagerDuty
    • Hermes
    • OpenClaw
    • buzz.xyz

Success on your company’s work over time, with and without memco, the return on investment that builds on it, and the knowledge asset you own

Success on your company’s work

Success on your company’s work over timeTwo lines over time with three model releases marked. Your competitors’ line gains a little between releases and steps up at each release. The line for you with memco starts at the same point, climbs between releases as every finished task leaves a lesson behind, and takes the same steps from a higher base. The gap between the lines widens quickly, then keeps widening more slowly.

You, with memco

Your competitors

Same models.Your experience.

Return on investment

Return on investment over timeReturn on investment over the same time span as five rising bands stacked on each other: lower cost per result, engineering capacity, faster review cycles, first-pass acceptance, operational reliability. Each grows with the same learning curve as the success advantage, so the whole stack climbs up and to the right.
  • Lower cost per result
  • Engineering capacity
  • Faster review cycles
  • First-pass acceptance
  • Operational reliability

Knowledge asset you ownPortable across models and agents

The knowledge asset you own, over timeTwo lines over the same time span. The knowledge asset you own with memco rises steadily and carries straight through every model release. Without memco, model-specific tuning grows a little between releases and is mostly lost at each one.

Owned and portable

Tied to the model

Your advantage carries over.

Each upgrade lifts both lines. Only yours keeps climbing in between, the return compounds, and the knowledge asset you own keeps growing, portable across every model and agent.

Governed continual learning

One agent learns.The next starts ahead.

memco turns what works, what fails and what changes into shared company knowledge. Your agents use that experience to avoid repeat mistakes and do better work.

For coding and knowledge work. Across models and tools.

Reserve a place

Company-owned. Shared within your rules.

Where governed continual learning fits

Company-governed reuse

Shared knowledge

Teams find what was recorded.

memco

Governed continual learning

Company knowledge improves with use.

Saved context

An agent remembers.

Agent learning

A workflow improves.

Individual use

Recalls contextLearns from work

memco combines related lessons, revises them as evidence changes and controls where they can be used.

How it works

How a lesson becomes company knowledge.

  • 01Combine

    Related lessons combine.

    Similar findings from different agents and tasks are combined into one lesson.

  • 02Revise

    SupersededCurrentFailed outcome

    Lessons change as evidence changes.

    Outcomes from later work revise a lesson or supersede it.

  • 03Control

    Shared within your rules.

    Lessons have a scope. Only authorised lessons cross it, so sharing follows your permissions.

  • 04Keep

    Change the model. Keep the learning.

    Learning stays separate from any one model, so switching models doesn’t start your company knowledge over.

  • 05Where it applies

    Coding and knowledge work.

    Across models and tools. For small teams, scaleups and enterprises.

  • 06Ownership

    Company-owned.

    The company’s experience should belong to the company, however the work gets done. People set direction and keep responsibility.

Your company's advantage

Stop paying for
the same lessons.

The frontier labs make general expertise easier to buy. Your competitors get the same upgrades. Your advantage is knowing what works in your business: the exceptions, decisions and working practices learned over years.

memco helps turn that know-how into knowledge your agents can use, test and improve.

Effort per accepted result · lower is better

Four agents inherit a better starting point with shared learningEffort index, from zero to one hundred, on a fixed acceptance standard. Four separate traces represent successive agents. With separate learning every agent starts at 100. With shared learning, the agents start at 100, 60, 48 and 40. Dashed traces retain the separate-learning comparison. Each trace covers the same amount of comparable work.
First agentNext agentNextNext

The next agent inherits a better starting point.

View the values

Each agent completes five equal steps of comparable work. The acceptance standard and axes stay fixed in both views.

Effort index across those five steps
Agent12345
Separate: all10080655550
Shared: first10080655550
Shared: next6049434038
Shared: next4840353230
Shared: next4034302827

A future scenario

99% agents.1% humans.Whose knowledge?

Imagine a company running 99 agents for every person. People set direction and keep responsibility. The company's experience should belong to the company, however the work gets done.

Switch models without starting your company knowledge over. memco keeps learning separate from any one model, with access set by your company.

Your company · 99 agents, one human authority

People set direction.

Policy · exceptions · accountability

Retained company asset

Company-owned knowledge

Related lessons combined. Revised as evidence changes.
Independent of the model doing the work.

Only authorised lessons cross scopes

33 agents · restricted local scope

Engineering

Frontier model

Architecture lessons stay within their authorised scope.

33 agents · restricted local scope

Customer work

Open-weight model

Policy exceptions are available to permitted agents.

33 agents · restricted local scope

Operations

Specialist model

Working practices remain under company access rules.

Each square is an agent. The diamond marks human authority.

Change the model mix.
The company knowledge stays.

Controlled research

Better results.Same model weights.

In a banking benchmark, agents improved on repeated tasks using shared memory and benchmark-provided solutions.

Read the research

Single-trial success

Static retrieval+ learned memory
97 banking tasks, four sequential trials.
What the comparison does and doesn't show

The learned-memory condition adds shared memory, reflection and verified correction instructions, with unchanged model weights. One Claude baseline trial was missing because of an infrastructure error. The reported rate averages each task's evaluable trials.

This does not establish unseen-task generalisation or enterprise cost savings. Source: Learning on the Job, Table 1.

Typical success metrics

What good looks like,measured.

Nine measures teams use to judge whether agents are getting better at the company’s work, and not just because the model improved.

  • First-pass acceptance

    The share of outputs that meet the customer’s standard without substantive correction or rework.

  • Time to an accepted result

    Elapsed time from starting a task to a usable outcome, including retries, review and handoffs.

  • Safe autonomous completion

    The share of agreed tasks completed correctly without unplanned human rescue. Required approvals still count as required.

  • Repeat-error rate

    How often a previously understood mistake recurs in later work where that lesson should apply.

  • Cross-person or agent learning gain

    How much better someone performs on a new task because of what another person or agent previously learned.

  • Adaptation speed

    How quickly affected people and agents act correctly after a policy, fact or operating condition changes, without breaking valid exceptions.

  • Time to first proven value

    Time from starting deployment to a verified improvement in real customer work, not merely the first memory stored or retrieved.

  • All-in cost per accepted result

    Model spend, human intervention, setup and knowledge maintenance together. More tokens can be worthwhile if they produce a better result.

  • Improvement with experience

    Whether comparable work becomes more reliable or needs less intervention as learning accumulates, rather than because tasks got easier or the model improved.

Put company learning inside your product.

Building coding agents or knowledge-work software for customers? Give each customer's agents a way to learn from their own work.

Explore the SDK

Build on what your company learns.

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