As AI systems enter production, reliability and governance can no longer depend on wishful thinking. Here’s how observability turns large language models (LLMs) into auditable, trustworthy enterprise systems.
Why observability secures the future of enterprise AI?
The enterprise race to deploy LLM systems reflects the early days of cloud adoption. Executives like promise; Compliance demands accountability; Engineers just want a paved road.
Yet, under the excitement, most leaders admit they can’t figure out how AI decisions are made, whether they helped the business, or whether they broke any rules.
Take a Fortune 100 bank that has deployed LLM to categorize loan applications. Benchmark accuracy looked stellar. Yet, 6 months later, auditors found that 18% of critical cases had been misreported without any warning or trace. The root cause was not bias or bad data. It was invisible. No observability, no accountability.
If you can’t observe it, you can’t trust it. And AI will silently fail without being noticed.
Visibility is not a luxury; This is the foundation of trust. Without it, AI becomes uncontrolled.
Start with results, not models
Most corporate AI projects start with technical leaders choosing a model and, later, defining success metrics. He is backward.
Reverse Order:
- First define the outcome. What is a measurable business goal?
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Deflect 15% of billing calls
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Reduce document review time by up to 60%
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Cut case-handling time by two minutes
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Design telemetry around that outcome, Nowhere around “Accuracy” or “BLEU Score”.
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Choose signals, recovery methods and models Which obviously transfers those KPIs.
For example, one global insurer turned a separate pilot into a company-wide roadmap by reframing success as “minutes saved per claim” instead of “model precision.”
A 3-layer telemetry model for LLM observations
Just as microservices rely on logs, metrics, and traces, AI systems need a structured observability stack:
a) Hint and context: what happened
- Log each prompt template, variable, and retrieved document.
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Record model ID, version, latency and token count (your key cost indicators).
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Maintain an auditable redaction log showing what data was hidden, when, and under what rules.
B) Policies and Controls: Guardrails
- Capture security-filter results (toxicity, PII), citation presence, and rule triggers.
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Store policy reasons and risk level for each deployment.
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Link outputs to governing model cards for transparency.
c) Results and Feedback: Did it work?
- Collect human ratings and edit distances from accepted answers.
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Track downstream business events, case closed, document approved, issue resolved.
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KPIs measure delta, call time, backlog, reopen rate.
All three layers connect through a common trace ID, allowing any decision to be replayed, audited, or improved.
Diagram © Saikrishna Kurapati (2025). Created specifically for this article; Licensed to VentureBeat for publication.
Apply SRE Discipline: SLOs and Error Budgets for AI
Service Reliability Engineering (SRE) transformed software operations; Now it’s AI’s turn.
Define three “golden signs” for each critical workflow:
| Signal |
target slo |
when the violation occurred |
|
factuality |
≥ 95% verified against source of records |
Return to verified template |
|
Security |
≥ 99.9% pass toxicity/PII filter |
Quarantine and human review |
|
utility |
≥ 80% accepted on first pass |
Retrain or rollback prompt/model |
If hallucinations or denials exceed the budget, the system auto-routes to safe signals or human review, such as re-routing traffic during a service outage.
This is not bureaucracy; Its credibility applies to logic.
Build thin observability layer in two agile sprints
You don’t need a six-month roadmap, just focus and two short sprints.
Sprint 1 (Weeks 1-3): Foundations
- Version-controlled prompt registry
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Reduction middleware is linked to the policy
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Request/response logging with trace ID
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Basic assessment (PII check, citation presence)
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Simple Human-in-the-Loop (HITL) UI
Sprint 2 (Weeks 4-6): Railings and KPIs
- Offline test set (100-300 real examples)
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Policy gateway for factuality and security
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Lightweight dashboard monitors SLO and cost
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Automated Token and Latency Tracker
In 6 weeks, you’ll have that thin layer that answers 90% of the governance and product questions.
mAK evaluation constant (and boring)
The assessment should not be a heroic one-off; They should be regular.
- Curated test sets from real cases; Refresh 10-20% monthly.
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Define clear acceptance criteria shared by product and risk teams.
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Run the suite weekly for further drift checks on each signal/model/policy change.
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Publish an integrated scorecard each week covering factuality, safety, usefulness and cost.
When evals are part of CI/CD, they stop being compliance theater and become operational pulse checks.
apply human inspection where it matters
Complete automation is neither realistic nor responsible. High-risk or unclear cases should be escalated for human review.
- Send low-confidence or policy-flagged responses to experts.
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Capture every edit and reason as training data and audit evidence.
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Feed reviewers’ feedback back into prompts and policies for continuous improvement.
At one health-tech firm, this approach cut false positives by 22% and produced a retrained, compliance-ready dataset in weeks.
CTake control through design, not hope
The cost of an LLM increases non-linearly. Budget won’t save you, architecture will.
- The structure indicates that deterministic sections run before generative sections.
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Compress context and re-rank instead of dumping entire documents.
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Cache frequently asked questions and remember tool output with TTL.
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Track latency, throughput and token usage per feature.
When observability covers tokenization and latency, cost becomes a controlled variable, no surprise.
90 day playbook
Observable Within 3 months of adopting AI principles, enterprises should see:
- 1-2 Production AI assists with HITL for edge cases
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Automated assessment suite for pre-deployment and nightly runs
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Shared weekly scorecard on SRE, product and risk
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Audit-ready trace linking signals, policies and outcomes
At a Fortune 100 client, this structure reduced incident time by 40% and aligned product and compliance roadmaps.
increasing confidence through observation
Observable AI is how you transform AI from experiment to infrastructure.
With clear telemetry, SLOs, and human feedback loops:
- Authorities receive evidence-supported convictions.
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Compliance teams get replayable audit chains.
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Engineers recuperate quickly and ship safely.
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Customers experience trusted, explainable AI.
Observability is not an added layer, it is the foundation of trust at scale.
Saikrishna Kurapati is a software engineering leader.
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