How Teams Use Looper

Deployment patterns, use cases, and economics for ongoing drift prevention

Deployment Patterns

Teams use Looper in three ways to prevent drift and catch unstable reasoning before it causes problems:

Pattern 1

🛡️ Inline Gating for Critical Decisions

When: Before any high-stakes action (refunds, account blocks, workflow triggers, legal summaries)

How it works:

  1. Generate 2-3 reasoning attempts for the decision
  2. POST to /score
  3. If risk_band == "high" → reject or retry
  4. If risk_band == "low" → proceed with confidence

Drift benefit: If a model update causes unstable reasoning, risk events spike immediately—letting you detect drift before customers notice.

Pattern 2

📊 Scheduled Drift Monitoring

When: Daily or hourly automated checks (the "reasoning heartbeat")

How it works:

  1. Run 100-300 fixed prompts through your system
  2. Generate reasoning variants for each
  3. POST to /score
  4. Track: average reliability, % high-risk cases, winner stability

Drift benefit: Detect stability drops even when accuracy is unchanged. This is Datadog + PagerDuty for LLM reasoning.

Pattern 3

🔬 Continuous Sampling for High-Volume Workloads

When: Large pipelines (support, analytics, agents) that process thousands of requests

How it works:

  1. Sample 5-10% of production traffic
  2. OR gate only critical steps (agent tool calls, high-risk branches)
  3. Send samples to /score
  4. Monitor for task drift, domain drift, model version changes

Drift benefit: Get visibility into reasoning instability hot spots without scoring every request.

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Economics: Is Looper Worth It?

Very Economical

High-Stakes or Regulated Tasks

Industries: Fintech, healthcare, insurance, enterprise agents, legal, trust & safety

Why they pay: A single bad action costs $100-$10,000+ in liability, compliance violations, or damaged customer relationships. Looper calls cost fractions of a cent.

Economics: One prevented failure covers months of Looper costs.

Economical with Sampling

High-Volume, Lower-Risk Tasks

Examples: Customer support deflection, content transformation, RAG-based search, summarization

Why they pay: Don't need full coverage—use sampling (1-10% of traffic) or gate only tricky flows.

Economics: Cost is manageable. They pay for confidence, not full coverage.

Best Suited for Free Tier

Consumer-Grade & Experimental Workloads

Examples: Casual chatbots, entertainment apps, hobby projects, research experiments

Approach: These use cases are perfect for our playground and /score_demo endpoint. Great for learning and experimentation.

Economics: For production deployments, consider upgrading when stakes or volume increase.

Use Cases Ranked by Value

🥇 #1
Agents
Multi-step decision-making. Most failures come from contradictory or unstable reasoning. Perfect fit for checking each step and blocking unsafe actions.
🥈 #2
Support Bots
Expensive when wrong, embarrassing when wrong, hard to monitor. Looper gates unsafe responses and detects drift after vendor updates.
🥉 #3
Finetuned Models
Finetunes drift over time. Without Looper, teams only see correctness drop—often too late. Catch quality degradation early.
#4
High-Volume Pipelines
Classification, content moderation, extraction, code analysis. Benefit from Looper samples (cheap and effective).
#5
Offline Eval Pipelines
Drop-in scoring layer for academic and enterprise evals. Replaces manual review and overly simplistic accuracy-only metrics.

Why Continuous Monitoring Matters

Models drift. Agents hallucinate silently.

Reasoning becomes unstable before accuracy changes. LLM providers update models unpredictably. Finetunes degrade over time. Complex pipelines break in subtle ways.

Looper is economical because:

The Value Proposition:

"Companies use Looper not to make their models smarter, but to make them safer. Looper gives them the missing signal—reasoning stability—which detects drift and prevents silent AI failures. It only needs a small amount of traffic or scheduled sampling to provide real value, and for high-stakes tasks, Looper becomes a necessary guardrail."

Real-World Examples

Agent Action Gating

Scenario: An agent decides whether to approve a $500 refund.

Implementation:

  1. Agent generates reasoning for "approve refund"
  2. Agent re-deliberates and generates alternative reasoning
  3. POST both to /score
  4. If risk_band == "high" → escalate to human
  5. If risk_band == "low" → auto-approve

Result: Prevents costly errors before they execute.

Support Bot Drift Detection

Scenario: Customer support bot handling 10,000 tickets/day.

Implementation:

  1. Sample 5% of responses (500/day)
  2. For each sample, generate original + self-check reasoning
  3. POST to /score
  4. Track daily average reliability_score

Result: When vendor updates their model, stability drop is detected within 24 hours.

Scheduled Monitoring for Critical Systems

Scenario: Financial compliance agent runs daily.

Implementation:

  1. Nightly job runs 200 fixed prompts
  2. Generate 2 reasoning variants per prompt
  3. POST all to /score
  4. Alert if >10% are high-risk (up from baseline 2%)

Result: Catches finetune degradation before it affects production.

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