AI Remediation
Every failure ends with an owner and a date
See what happened, understand the likely cause, and build a practical plan to test the change.
- Expected
- Exchange the order
- Actual
- Refunded the order
- Key moment
- The user changed the request, the workflow kept the first intent
- Context
- Conversation · Trace · Tool calls · Outcome
The flow
From outcome to validated change
See the whole failure in one place
The system can stay online, return a valid response, and still do the wrong job. Connect the conversation, state, retrieval, tools, timing, and final outcome.
Know where the path changed
Trace the issue to a prompt, missing source, retrieval problem, stale state, tool choice, parameter, guardrail, or workflow step.
Move from explanation to a practical change
Review suggested prompt, retrieval, Knowledge Base, guardrail, or workflow changes. Your team decides what to apply — and the incident, owner, and validation result are preserved.
Validate the change
The fix is not done until the situation passes
Run the same case again, compare the result, and keep the scenario for future releases.
- Before
- Refund started · Failed
- After
- Exchange started · Passed
- Kept as
- Regression scenario
The backlog
A finding with an owner, a fix and an expected number
This is the whole difference between monitoring and remediation. Every row below arrived from a cluster of failing sessions and left carrying a suggested change, a named team, a date, and the specific metric it is expected to move — so it can be argued with, scheduled, and checked afterwards.
Every row names a team and a date and states the number it expects to move. Item 3 already shipped and hit its target — 7.1% timeout down to 2.2%. That is what separates a backlog from an alert feed.
| # | Issue and evidence | Sev | Suggested fix | Owner | Expected impact | Status |
|---|---|---|---|---|---|---|
| 1 | Fee-exception answers missing tier exception clause214 failed sessions · 4-turn avg loop | HIGH | Update KB article, add retrieval tags, revise answer template | Knowledge Eng | Fee-exception reliability 63% → ~80% | In progressJul 11 |
| 2 | Dispute escalation routed to generic help flow68 low-sat sessions · 167-question cluster | HIGH | Adjust workflow routing, add escalation branch | Product Eng | Intent reliability 68% → ~82% | PlannedJul 18 |
| 3 | Account verification tool timeout7.1% timeout rate on verification span | HIGH | Retry logic + timeout threshold update | Platform Eng | Timeout 7.1% → 2.2% observed | ShippedValidated |
| 4 | Policy refusal over-fires on benign fee questions146 false refusals | MED | Refine refusal criteria, validate against replay set | AI Eng | Recover ~120 completions/month | PlannedJul 15 |
| 5 | Latency spikes on document lookupp95 rose to 5.4s in one intent cluster | MED | Cache top retrieved chunks | Infra Eng | p95 5.4s → <3.5s | PlannedJul 22 |
Each item links to its evidence set: the clustered sessions, the traces, the failed evaluations and the root-cause classification that generated it. When it ships, the validation result attaches to the same record — so the item history reads as an audit trail from detection to proof.
Figures from a sample FinVault reliability report. Design-partner data, synthetic and illustrative of real deliverables.
The gate
Nothing reaches production without human approval
4,050 failed sessions → 7 human approvals → 5 shipped changes. PRISM finds the failure and proposes the fix. A human decides what ships.
4,050
Failures clustered
3,822
Root causes classified
14
Backlog items opened
9
Fixes proposed
7
Human approved
5
Shipped & validated
Knowledge and guardrails
Review the change, the affected sessions, and the evidence before approving.
Prompts
Every prompt change is versioned, diffable, and reversible.
Code
PRISM drafts the change and the evidence. You open the pull request. Nothing merges without you.
The proof
Re-measured on the same cohort that found the problem
A fix is only Validated when the target metric improves in production over a minimum seven-day window, measured against the same cohort definition used to detect the failure. Anything that misses its primary target is marked partially validated and opens a follow-up automatically.
Knowledge · Jun 9
Refund policy KB update
Session success rate
BEFORE61%AFTER79%Validated
Code · Jun 14
Verification retry · PR #241
Tool timeout rate
BEFORE7.1%AFTER2.2%Validated
Prompt · Jun 21
Dispute prompt v2.3.0
Average turns to resolve
BEFORE3.9AFTER2.4Partially validated
One item, opened
From a cluster of failing sessions to a merged pull request
The record is continuous. Detection, cause, proposed change, approval and validation are four states of one object rather than four tickets in four systems that nobody reconciles.
Backlog item #3 — the verification timeout — from 7.1% to 2.2% and closed. The same record carries all four states.
The evidence never detaches
Open the item at any point and the clustered sessions, the traces, the failed evaluations and the classification that produced it are still one click away. That is what makes the recommendation arguable rather than something to accept.
The expected lift is stated before the work
“Timeout 7.1% → under 2.5%” was written when the item was opened, not after it shipped. A projection you record beforehand is the only kind that can be wrong, which is the only kind worth having.
A partial result opens the next item
The dispute-flow prompt change reduced turns but missed its resolution target. It was marked partially validated and generated the follow-up automatically — which is how the residual issue got identified as workflow logic rather than prompt copy.
Product boundary
PRISM supports root-cause investigation, failure explanation, remediation recommendations, a remediation backlog, before and after validation, and human review and approval. It does not generate autonomous pull requests, make unattended repository changes, merge automatically, deploy automatically, or perform autonomous production remediation.
Questions
Common questions
Does AI Remediation edit my code?
Not by default. It brings the failure and likely cause into one place and recommends a next action. Your team controls the change.
What kinds of changes can it suggest?
Recommendations may cover prompts, retrieval, Knowledge Base content, guardrails, workflow state, tool use, and the validation plan.
How is a failure validated?
Run the same scenario or representative trace after the change and compare the expected and actual result.
Can I keep a remediation history?
The product should preserve the incident, recommendation, owner, status, evidence, validation result, and relevant audit history according to plan and retention limits.
Is this only for production issues?
No. The same flow can begin from a synthetic scenario, an imported conversation, an evaluator result, or a live production trace.