On the record
Every learning, approval and rule change is attributed and kept.
Learnings
Agents that get better every day.
Learnings turns every run into signals, finds what's going wrong across your fleet, and proposes the fix. Approve it once, and every agent follows it from the next run.
Learnings · Support Studio · Last 30 days
IllustrativeAgentic health
94%
+6 pts this month
4,212
Runs analyzed
5
Learnings ready
12
Rules added
ComplianceReliabilityLatencyCostRule failures
1
Rule failing7 of 20 runs
Refund policy not cited before approval
2
Redundantadds latency and cost
18 rules never fired in 60 days
3
Driftimpact high
Cost per run 41% above this agent's baseline
4
Missing rulerecommended
Escalate refunds above the cap to a person
5
Reliabilityimpact low
Lookup tool timing out on 1 in 12 runs
Selected learning
Refund policy not cited before approval
Root cause
The refund policy section isn't in the agent's context.
Proposed fix
Add section 4.2 to the agent's context and require a citation.
Evidence
Run 398Run 401Run 405+4
Flag for fixAdd ruleCode fixChange contextWatch
Apply to
This run
This agent
Every agent in the Studio
Why it matters
Your agents run faster than anyone can review them.
Review coverageToday
Your team reviews about 1 in every 100 runs.
ReviewedNever reviewedThe other 99, nobody looks at.
AISquare · Learnings
Refund agentLiveRun history · Last 40 runs
40 runsWithout Learnings
Fixed by hand 3 times. Still comes back. Run #324 knows no more than run #1.
With Learnings
Approved
Pattern found at run 7. Approved once. Gone from every agent after.
Without Learnings
- 10 mistakes
- 3 fixes by hand
- Still recurring
With Learnings
- 1 mistake
- 1 approval
- 0 repeats
MistakeFixed by handApprovedClean run
Observability shows you what went wrong. Without Learnings, someone still has to find it, fix it and remember it, run after run.
How Learnings works
Detect. Decide. Deliver.
01 · Detect
Find the patterns one run can't show.
Learnings reads every run and groups what keeps going wrong, so you see causes, not alerts.
- Rules that keep failing, with the root cause
- Rules that never fire, adding latency and cost
- Cost and speed drift against each agent's baseline
AISquare · Learnings
Support Studio/Learnings
Last 30 daysAll agentsOpen · 5
LearningCategoryRunsImpact
Refund policy not cited before approval
Rule failing7 of 20 runs18 rules never fired in 60 days
RedundantReview rulebookCost per run 41% above baseline
Drift30 day trendEscalate refunds above the cap
Missing ruleRecommendedLookup tool timing out
Reliability1 in 12 runsAISquare · Review
Proposed learning
Always cite the refund policy section before approving a refund.
Action
Flag for fixAdd ruleCode fixChange contextWatch
Apply to
This runThis agentEvery agent in the Studio
Evidence
Run 398 · 401 · 405 +4
Reviewer
Support lead
Status
Pending approvalApprove and add to rulebook
Kept on the record
02 · Decide
Nothing changes until your expert says yes.
Each learning comes with evidence and a proposed fix. Your expert chooses what to do and how far it goes.
- Flag, add a rule, change context or send a code fix
- Apply to one run, one agent or every agent in the Studio
- Every decision is attributed and kept on the record
03 · Deliver
The rule applies from the next run.
Approved fixes join the rulebook with no retraining or redeploy. Engineering gets a brief it can act on.
- Enforced on the next run, at the scope you chose
- A repair brief for your developer or coding agent
- The same insights over the API and the learnings MCP server
repair-brief.md
repair-brief.md
Markdown
## What's happening
A rule fails on 7 of the last 20 runs.
## Why
The agent doesn't have the policy section in context.
## Suggested fix
Add section 4.2 to the agent's context.
## Evidence
Runs 398, 401, 405 and 4 more.
insights API
curl -s -H "X-API-KEY: $EXPLAINABILITY_API_KEY" \
"$EXPLAINABILITY_GATEWAY_URL/v1/studios/$STUDIO_ID/insights?window=20"The same insights are available to your tools. Read the docs
Built for enterprise
Governed by design.
Human approval
Nothing reaches your agents without an expert's sign-off.
Your rulebooks
Approved fixes land in the rulebooks you already run.
Scoped and screened
Learnings stay in your workspace, with sensitive data screened before anything is saved.
Why it matters
Finding issues is the easy part.
Dashboards
Show you what went wrong. You still have to read them.
Issue trackers
Suggest a fix. Someone still has to apply it by hand.
Learnings
Turns an approved fix into a rule the agent follows from the next run.
FAQ
Questions about how agents learn and improve.
Does Learnings change my agents automatically?
No. Learnings proposes fixes. Nothing changes until one of your experts approves, and every approval stays on the record.
How does a learning become a rule?
When a pattern repeats across runs, Learnings proposes a fix with the evidence behind it. Once your expert approves, it joins the rulebook and the agent follows it from the next run, with no retraining.
What does it watch?
Cost, speed, reliability and rule results on every run, compared with each agent's own baseline over 7, 30 and 60 day windows.
How far can a fix go?
You choose: just that run, that agent, or every agent in the Studio.
How is this different from evals?
Evals score outputs. Learnings finds the patterns behind them and turns an approved fix into a rule your agents follow.
What is agentic health?
One score per agent that rolls up rule failures, drift, reliability, redundant rules and missing rules, so you can see at a glance which agents need attention.
Can a coding agent use it?
Yes. Learnings writes a repair brief a developer or coding agent can act on, and the learnings MCP server exposes the same reads to Claude Code, Cursor and any MCP client.
Is it available on every plan?
Learnings is available to all AISquare customers.
Stop making the same fix twelve times.
Start your journey with AISquare
Connect an agent and let every approved fix make the next run better.