Works with
Claude Code · Cursor · Codex · Copilot
Campaign · coding-agent rework
Make the mistake once.
Make the mistake once.
Teach the team forever.
Your agents keep relearning your repo — the same failed paths, the same review notes, the same dead ends. Memco captures the correction once, then hands the next agent the shortcut your team already paid to discover.
Session 01
Rediscovers the repo quirk
✗ paid · learned 0×
Session 02
Walks the same failed path
✗ paid · learned 0×
Session 03
Hits the same review note
✗ paid · learned 0×
With Memco
Captured once. Reused forever.
✓ pay once
Stop agents repeating
the same mistakes.
Your team already paid for the fix once. Memco captures the repo quirks, failed paths, review comments, and human corrections behind successful agent work — then gives the next agent the shortcut.
Scope
Team & org memory
Governed
Provenance · scope · audit · decay
01The hidden cost
The cost isn’t tokens. It’s the work you pay for twice.
Every agent run creates learning — a quirk discovered, a path ruled out, a review comment internalised. Today most of that learning dies when the session ends, so the next agent rediscovers it on your dime.
Repeat 01
Same setup questions
Each new session relearns how the repo is wired, where things live, and how to run it.
Repeat 02
Same failed commands
The dead-end build step, the wrong test runner, the deprecated flag — rediscovered run after run.
Repeat 03
Same bad assumptions
An agent guesses the architecture wrong the same way it did last week, in a different IDE.
Repeat 04
Same review comments
Reviewers re-explain the same preferences, conventions, and rejections across PRs.
Repeat 05
Same internal-API confusion
The legacy wrapper, the gotcha endpoint, the auth quirk — relearned from scratch each time.
Repeat 06
Same migration mistakes
A migration breaks for a reason you already diagnosed once. Nobody told the next agent.
02The loop
One loop loses the lesson. The other compounds it.
The difference between an agent rollout that plateaus and one that gets sharper every week is whether a correction survives the session it happened in.
Today · the lesson disappears
Task → context reload → wrong path → human fix → lesson lost.
With Memco · the lesson sticks
Agent hits issue → human corrects → Memco captures → reviewer scopes → next agent retrieves.
Make the mistake once. Teach the team forever.
03What Memco remembers
Concrete lessons, not vague “knowledge.”
These are the kinds of memories Spark captures from real agent work, scopes to the right repo or team, and serves back the next time an agent is about to repeat them.
Repo convention
“Usepnpm test:unit, notnpm test, in this repo.”
Failed path
“Do not call the legacy auth wrapper directly — it bypasses tenant scoping.”
Flaky test fix
“This flaky test needs the mocked clock reset before each run.”
Migration scar
“Previous migration failed because the generated SQL missed tenant scoping.”
Review rejection
“Security review rejected this pattern last time — use the signed-URL helper.”
Human correction
“The retry budget here is 3, not the default 5 — downstream rate-limits.”
04How it works
How a one-off correction becomes governed memory.
No repo upload, no code training. Spark watches the work, extracts the useful signal, and only promotes what passes review and earns trust.
01
Capture useful agent work
The agent hits an issue and a human corrects it. Spark captures the lesson from the run — not the source.
02
Promote the lesson
A reviewer or policy approves it, sets scope, and turns a one-off fix into a reusable, typed memory.
03
Govern trust & freshness
Provenance, trust score, and decay keep memory current. Stale or contradicted lessons fade out.
04
Reuse before repeating
The next agent — any tool, any model — retrieves the lesson before it walks into the same wall.
Docs say what should be true. Memco remembers what actually happened during the work — including the corrections no one wrote down.
05Proof in numbers
Less repeated discovery. Faster, cleaner runs.
48%
faster task completion with shared memory
98%
recommendation hit rate on retrieved lessons
53%
fewer tokens per task — proof, not the pitch
1×
you pay to discover each mistake exactly once
Figures are illustrative of results seen in controlled evaluations (DS-1000 / Spark benchmark family) and early deployments; your numbers depend on stack, repos, and workflow. Code stays yours. Lessons travel.
06Book a session
Map your repeated-agent failures.
A focused 25-minute working session. We map where your agents repeat themselves today and what a memory loop would catch first. No repo access, no code upload, no proprietary data — pattern-level only.
Repeated-failure mapping
25 minutes. Your stack, your repeats.
Bring the workflows where your coding agents keep relearning the same things. We’ll show where capture, scope, and reuse would land.
- No repo or code upload
- Works with your current tools
- Leave with a concrete first scope
Turn coding-agent power users into a team advantage.
Memco captures the patterns, prompts, repo knowledge, review preferences, and fixes discovered by your best agent users — then makes them reusable across the whole team.
Works with
Claude Code · Cursor · Codex · Copilot
Scope
Personal · repo · team · org
Governed
Review · provenance · audit
01The rollout problem
Power users sprint ahead. Everyone else starts cold.
Most AI coding rollouts create a gap. A handful of engineers get huge gains; the rest of the org never inherits the workflow, and the advantage stays trapped in a few people’s heads.
Gap 01
Prompt hacks stay personal
The prompts that actually work live in one engineer’s scratchpad, never shared, never reused.
Gap 02
Repo tricks stay in one head
The person who knows how to drive an agent through this codebase is a single point of failure.
Gap 03
Working workflows don’t spread
A proven agent workflow on one team never makes it to the team next door.
Gap 04
New hires still onboard manually
Every new engineer re-learns the same context a senior already taught their agent months ago.
Gap 05
Teams standardize too slowly
Without a shared memory layer, conventions drift and every team reinvents the same playbook.
Gap 06
The advantage doesn’t compound
You bought the tools for everyone, but the gains stay concentrated in the few who figured them out.
02The rollout model
Discover once. Inherit everywhere.
Memco turns what your best agent users discover into validated, scoped memory the rest of the team can rely on — without anyone hand-writing a wiki.
01
Power user discovers a pattern
A strong agent user finds the prompt, fix, or repo trick that works.
02
Memco captures the lesson
Spark extracts the reusable signal from the run — not the source code.
03
The team validates & scopes
A reviewer promotes it to repo, team, or org scope. Trust and freshness are tracked.
04
Everyone’s agents inherit it
Relevant engineers — and their agents — retrieve the approved lesson automatically.
Power users discover the pattern. Memco lets everyone inherit it — so your rollout stops depending on prompt folklore.
03Memory scopes
Small teams need speed. Large teams need speed plus boundaries.
Scope controls who a lesson reaches. Start loose for a single repo, tighten as you roll out across business units — same primitives at 5 engineers or 40,000.
Scope 01
Personal
An engineer’s own working memory across their sessions and tools.
private
Scope 02
Repo
Lessons that apply to a specific codebase — conventions, quirks, test commands.
shared in repo
Scope 03
Team
Validated patterns and review preferences the whole team relies on.
promoted
Scope 04
Org
Cross-cutting standards, security rules, and architecture constraints.
governed
04Example rollout
A four-week rollout you can actually measure.
You don’t need a six-month platform programme to find out whether shared memory helps. Start with one repo and one team.
W1
Capture
One repo, five engineers. Capture recurring fixes, quirks, and review comments from real work.
W2
Promote
Promote the top lessons into team memory. Set scope, review, and trust defaults.
W3
Measure
Measure reuse across tasks and engineers: repeated discovery, review loops, completion.
W4
Expand
Expand to a second repo or workflow once the loop is proven on the first.
Decide from the numbers. One agent learns. Every engineer ships faster.
05Proof in numbers
The gains stop being personal and start being a team property.
48%
faster task completion with shared memory
98%
recommendation hit rate on retrieved lessons
53%
fewer tokens per task across the team
1
repo and one team is all you need to start
Figures are illustrative of results seen in controlled evaluations (DS-1000 / Spark benchmark family) and early deployments; your numbers depend on stack, repos, and workflow. Code stays yours. Lessons travel.
06Book a session
Plan your team rollout.
A coding-agent rollout mapping session. We’ll map your power-user patterns, the gap to the broader team, and the first repo to roll memory out on. No repo access, no code upload.
Rollout mapping
Plan the first four weeks.
Bring the team that’s ahead and the team that isn’t. We’ll map capture, validation, and scope for a measurable rollout.
- No repo or code upload
- Works with your current tools
- Leave with a 4-week plan
Give agents memory without creating shadow context.
Memco gives teams private, scoped, auditable memory for agent work — with provenance, approval, permissions, and freshness controls built in. Before every tool grows its own memory, define the company layer.
Control plane
RBAC · SSO/SAML · audit
Lifecycle
Provenance · approval · decay
Deploy
SaaS · VPC · on-prem
01The new governance problem
Agents are starting to remember. Who governs that memory?
Agents now carry work across sessions, tools, repos, and teams. If that memory is unmanaged, it becomes another shadow system — one with no owner, no audit trail, and no lifecycle.
Risk 01
Unapproved context reuse
Memory written by one agent gets reused by another with no approval step in between.
Risk 02
Stale guidance
Last quarter’s “truth” keeps getting served long after it stopped being correct.
Risk 03
Unknown provenance
Nobody can say which run, agent, or human a given memory actually came from.
Risk 04
Tool-specific silos
Each vendor grows its own memory surface. The company never sees the whole picture.
Risk 05
No audit trail
No record of what was read, written, promoted, or revoked — and by whom.
Risk 06
Leakage across teams
Sensitive lessons cross boundaries they should never cross, with no deletion lifecycle.
02Governance primitives
Memory with a lifecycle, not a dumping ground.
Eight primitives turn raw agent output into governed organizational memory. Trust is auditable, not asserted.
◹
Scope
Personal, repo, team, or org. Crossing a boundary is opt-in and explicit.
⟶
Provenance
Every memory traces to the run, agent, and human correction that produced it.
◑
Trust
A Bayesian trust score grows from real evidence — what helped, what got corrected.
✓✗
Approval
Human-in-the-loop review before a lesson is promoted from team to org scope.
≈
Freshness
Memories carry recency signals so current guidance outranks the obsolete.
↓
Decay
Stale or contradicted memories fade and can be removed on a defined lifecycle.
≡
Audit
Every read, write, promotion, and revocation is logged and exportable to your SIEM.
⌐
Permissions
RBAC down to a memory entry. Promote, scope, or revoke as a control-plane action.
03What each side gets
Control for IT. Continuity for engineering.
The point of governance isn’t to slow engineers down. It’s to let them share the lesson without turning memory into a liability.
What IT / admin gets
A control plane, not a black box.
SSO / SAMLRBACOrg & team spaces
Admin controlsAudit trailsData residency
On-prem / VPCSIEM export
Your IT team can administer the system without reading the content of team memory where that separation is configured.
What engineering gets
Memory that helps, without workflow drag.
Agents remember lessonsNo workflow disruption
Only relevant memoriesFaster onboarding
Fewer repeated mistakesSame tools & models
Engineers keep their tools and their flow. What changes is that useful lessons stop dying at the end of the session.
Agent memory without audit is shadow infrastructure. Memco makes it a governed company asset.
04Posture & proof
Enterprise control, without losing the compounding benefit.
100%
of reads & writes captured in the audit trail
4
scope levels: personal, repo, team, org
3
deployment modes: SaaS, VPC, on-prem
0
training on your code, prompts, or completions
Capabilities reflect the Spark governance model; specific certifications (e.g. SOC 2) and deployment options are confirmed during enterprise evaluation. Code stays yours. Lessons travel.
05Book a review
Review your agent memory posture.
A governance review with our team. We map where unmanaged agent memory is forming today and what scope, provenance, approval, and audit would need to cover. No repo access, no code upload.
Governance review
Posture, gaps, controls.
Bring your security and platform leads. We’ll map the control plane against your existing SSO, RBAC, audit, and residency requirements.
- No repo or code upload
- Maps to your existing controls
- Leave with a posture summary
Your AI stack will change. Your memory should survive it.
Memco keeps your agent memory independent from any single model, IDE, or agent framework — so the lessons your team learns move across Claude Code, Cursor, Codex, Copilot, open models, and internal agents.
Models
Claude · GPT · Gemini · Llama · Qwen
Tools
Claude Code · Cursor · Codex · Copilot
Owned by
You — not the vendor
01The vendor-memory trap
Vendor memory helps one product. Your team works across many.
Memory that lives inside a single product helps inside that product. Engineering memory has to survive switching IDEs, model providers, and policies — or it isn’t really your memory.
Trap 01
Switching IDEs resets context
Move from one IDE to another and the accumulated working memory is gone.
Trap 02
Providers change behavior
A model update shifts behavior; the guidance baked into one vendor doesn’t move with you.
Trap 03
Frontier access changes
Pricing, limits, and policy shift. Your stack adapts — your learning shouldn’t have to restart.
Trap 04
Team tools fragment
Different teams standardize on different tools. Knowledge splinters across surfaces.
Trap 05
Knowledge gets trapped
The most valuable lessons end up locked inside one vendor’s memory feature.
Trap 06
Lock-in by accumulation
The longer you stay, the more learning you’d lose by leaving. That’s a switching cost you didn’t choose.
02What should be portable
The durable asset isn’t the model call. It’s what you learned.
These are the lessons that should move with your team regardless of which model or tool produced them.
lessonRepo lessons
lessonHuman corrections
lessonApproved workflows
lessonTest commands
lessonArchitecture constraints
lessonFailed paths
lessonInternal API usage
lessonTeam preferences
lessonSecurity rules
lessonMigration scars
03How Memco fits
The model reasons. Memco carries the company memory.
Memco sits below the runtime. Tools and models do the reasoning; Memco holds the company-specific learning — so the runtime can change without wiping organizational knowledge.
01
Tools reason
Whatever IDE, agent, or model is in play handles the reasoning for the task at hand.
02
Memco carries memory
The company-specific lessons live in Memco, independent of any one runtime.
03
Models can change
Swap providers or add open models — the organizational learning stays exactly where it is.
04
No cold starts
A new tool or model joins the stack already aware of your team’s hard-won context.
Claude today, Codex tomorrow, internal agents next quarter. The runtime will change. The learning should survive it.
04Why this matters now
Teams will run portfolios of models. Don’t let learning live in one of them.
6+
model families read & write the same memory
0
cold starts when you add or swap a runtime
48%
faster task completion with memory in the loop
53%
fewer tokens per task — support, not the pitch
Figures are illustrative of results seen in controlled evaluations (DS-1000 / Spark benchmark family) and early deployments. We do not claim an open model beats a frontier model; memory narrows the gap by reusing context regardless of model choice. Code stays yours. Lessons travel.
05Book a walkthrough
See model-portable memory in action.
A model-portability walkthrough. We map your current agent stack, where lock-in is forming, and how the same memory would serve every model and tool you use. No repo access, no code upload.
Portability walkthrough
Map your stack.
Bring the tools and models in play today and the ones you’re evaluating. We’ll show how one owned memory layer serves all of them.
- No repo or code upload
- Covers every model you run
- Leave with a portability map
Long-running agents need long-running memory.
Memco gives agents continuity across attempts, dead ends, corrections, handoffs, and retries — so longer tasks don’t collapse into loops. Bigger context helps the next prompt. Memory helps the next attempt.
Continuity across
Attempts · retries · handoffs
Captures
Dead ends · constraints · checkpoints
Keeps
Review in the loop
01Why long-running agents fail
A bigger context window is not continuity.
Multi-hour agents, background agents, subagents, and autonomous PRs all hit the same wall: without durable memory of the trajectory, long tasks collapse into loops.
Failure 01
Repeat the same strategy
The agent tries the approach that already failed because nothing recorded that it failed.
Failure 02
Lose correction context
A human corrected it an hour ago; that correction is no longer in the working window.
Failure 03
Forget why a path failed
The reasoning behind a dead end evaporates, so it gets re-walked from the top.
Failure 04
Restart cold after interruption
An interruption or timeout resets progress to zero instead of resuming from state.
Failure 05
Produce almost-right PRs
The output is 90% there but misses the constraint that was learned mid-task.
Failure 06
Can’t hand off cleanly
Passing to another agent means re-dumping the whole transcript — or losing the thread.
02Memory for agent trajectories
Typed memory of the run — not a chat log.
Memco captures the structured state a long task actually needs to resume: what was tried, what failed, what constrains it, and what to do next.
Type 01
Checkpoint
A durable marker of progress an agent can resume from after interruption.
Type 02
Failed approach
The path that didn’t work and why — so it isn’t re-attempted.
Type 03
Constraint
A limit discovered mid-task that the final output must respect.
Type 04
Reviewer correction
A human fix turned into a guardrail for the rest of the trajectory.
Type 05
Test result
What passed, what flaked, and what each result implies for the next step.
Type 06
Dependency warning
A risky dependency or version pin the agent must keep in view.
Type 07
Task summary
A compact state of the work so far — the useful part, not the transcript.
Type 08
Next-step rec
The recommended next move, ready for the same agent or a fresh one.
03The handoff model
Resume with the useful state — not the entire transcript.
When one agent hands off to another, Memco passes a scoped checkpoint: what matters to continue, without re-streaming hours of context.
Agent A · learns
Works the task, hits dead ends, and records the trajectory.
Agent B · resumes
Picks up from the scoped checkpoint and finishes.
If agents run for hours, their memory can’t be a chat transcript.
Memco doesn’t remove review — it makes review compound. Every reviewer correction becomes a guardrail the rest of the trajectory respects.
04Proof in numbers
Fewer loops, cleaner handoffs, tasks that actually finish.
48%
faster task completion with memory in the loop
98%
recommendation hit rate on retrieved lessons
53%
fewer tokens per task — no transcript re-dumps
1
scoped checkpoint to hand off, not the whole log
Figures are illustrative of results seen in controlled evaluations (DS-1000 / Spark benchmark family) and early deployments; your numbers depend on stack, task length, and workflow. Code stays yours. Lessons travel.
05Book a session
Map your long-running agent workflows.
A long-running agent memory session. We map where your longer tasks loop or stall today and what trajectory memory would catch — checkpoints, dead ends, handoffs. No repo access, no code upload.
Trajectory mapping
Where long tasks break.
Bring your background agents, autonomous PR flows, or multi-hour tasks. We’ll map where continuity would help most.
- No repo or code upload
- Works with your current agents
- Leave with a trajectory map
How reliable is your agent memory stack?
Take a 3-minute diagnostic to see whether your team’s agents actually learn from prior work — or just reload context and repeat mistakes. Your agents have context. Do they have memory?
No repo access
Pattern-level answers only
No code upload
No proprietary data
Takes
About 3 minutes
Memory reliability — preview7 categories
Emerging
band 2 of 5 · example result
Capture72
Reuse38
Governance24
Provenance31
Freshness55
Portability29
Team adoption41
01What the audit asks
Ten multiple-choice questions. No uploads.
A quick read of how your agents capture, reuse, and govern what they learn. Here’s a preview of the questions — the live diagnostic scores your answers across seven categories.
Q1
Which coding agents and tools does your team use?
Claude CodeCursorCodexCopilotInternal
Q2
How often do agents repeat mistakes humans already corrected?
RarelyWeeklyDailyConstantly
Q3
Where do agent lessons live today?
Chat historyAGENTS.mdDocs / SlackPR commentsNowhere consistent
Q4
Can one engineer’s agent learnings help another’s agent?
Yes, automaticallyManuallyNo
Q5
Do you know which source produced a retrieved memory?
AlwaysSometimesNever
Q6
Can memory be scoped by repo, team, or org?
YesPartlyNo
Q7
Can stale memories decay or be removed?
YesNo lifecycle
Q8
Can admins audit what gets reused?
Full auditPartialNone
Q9
What happens if you switch from Claude to Codex / Cursor / internal?
Memory followsStarts cold
Q10
What would make your agent rollout more credible?
ReuseGovernancePortabilityProof
Answer the preview, then send the audit request below. Your answers are included with the request; no repo access or code upload.
02Your result
From fragile to compounding.
Every audit returns a band and a short list of the gaps holding your memory back — with a clear next step for each one.
01Fragile
02Emerging
03Team-ready
04Governed
05Compounding
Gap 01
Repeated mistakes
Agents keep relearning what a human already corrected.
Gap 02
No write path
Useful lessons are never captured back into anything reusable.
Gap 03
No provenance
You can’t say where a retrieved memory actually came from.
Gap 04
Tool-specific silos
Memory is trapped inside one vendor’s surface.
Gap 05
No freshness lifecycle
Stale guidance keeps getting served with no decay.
Gap 06
No cross-agent reuse
One agent’s learning never reaches another’s.
03Where your result sends you
The audit routes you to the fix for the pain you actually have.
Pick the weakest part of your stack and go straight to the campaign built for it — or book a guided audit and we’ll walk it with you.
Repeated mistakesStop agents repeating the same mistakes./stop-agent-rework →
Team rolloutRoll out coding agents across a team./team-rollout →
GovernanceGovern agent memory before shadow context./governed-memory →
Model portabilityKeep memory portable across models./model-portable →
Long-running tasksMake long-running agents reliable./long-running →
04Start the audit
Find the weak point in your AI coding rollout.
Run the 3-minute diagnostic, or book a guided audit and we’ll map your memory reliability with you. Either way: no repo access, no code upload, pattern-level only.
Guided memory audit
3 minutes, or with us.
Tell us where you are and we’ll send the diagnostic plus a guided read of your result against the seven categories.
- No repo or code upload
- Pattern-level answers only
- Routed to your weakest category