When AI systems enter production, reliability and governance cannot depend on wishful thinking. Here’s how observation turns large language models (LLMs) into reliable, trustworthy enterprise systems.
Why Observation Secures the Future of Enterprise AI
The enterprise race to deploy LLM systems mirrors the early days of cloud adoption. Executives love a promise. Compliance demands accountability. Engineers just want a paved road.
Yet, beneath the excitement, most leaders admit they can’t figure out how AI decisions are made, whether they help the business, or if they break a rule.
Take a Fortune 100 bank that hired an LLM to rank loan applications. The benchmark’s accuracy seemed stellar. Yet, after 6 months, auditors found that 18 percent of critical cases had gone wrong, without a single warning or trace. The primary reason was not bias or bad data. It was invisible. No observation, no accountability.
If you can’t observe it, you can’t trust it. And insecure AI will fail silently.
Visibility is not a luxury. This is the basis of trust. Without it, AI becomes disorganized.
Start with results, not models
Most corporate AI projects start with tech leaders choosing a model and later defining success metrics. It is backwards.
Reverse order:
Explain the results first. What is a measurable business objective?
Eliminate 15% of billing calls
Reduce document review time by up to 60%
Cut case handling time by two minutes
Design the telemetry around this result, Not around “accuracy” or “blue score”.
Select indicators, recovery methods and models Which clearly transfers their PI.
For example, at one global insurer, defining success as “savings per claim per minute” rather than “model health” turned an isolated pilot into a company-wide roadmap.
A 3-layer telemetry model for LLM observation
Just as microservices rely on logs, metrics, and traces, AI systems need a structured observability stack.
a) Indication and Context: What happened
Log every quick template, variable and retrieved document.
Record model ID, version, latency and token count (your known cost indicators).
Maintain an auditable redaction log showing what data is masked, when and by what rule.
b) Policies and Controls: Safeguards
Capture safety filter results (poison, PII), referral presence and governance triggers.
Store policy reasons and risk levels for each deployment.
Link outputs from Governing Model Card for transparency.
c) Results and Feedback: Did it work?
Collect human ratings from accepted answers and edit distances.
Track downstream business events, case closed, document approved, issue resolved.
Measure KPI Delta, Call Time, Backlog, Reopen Rate.
All three layers are connected by a common trace ID, enabling any decision to be replayed, audited or improved.
Diagram © Sairashana Kovarapati (2025). Created specifically for this article. Licensed to VentureBeat for publication.
Apply the SRE discipline: SLOS and error budgets for AI
Service Reliability Engineering (SRE) changed software operations. Now it’s AI’s turn.
Define three “golden signals” for each critical workflow:
hint | Target SLO | When the violation occurred |
The facts | Verified by recorder 95% records | Fallback to the verified template |
Safety | .9 99.9% pass toxic/PII filters | Quarantine and human evaluation |
utility | ≥ 80% accepted on first pass | Retraining or rollback prompt/model |
If deception or denial exceeds the budget, the system auto-routes for safe signaling or human review, such as rerouting traffic during system outages.
This is not bureaucracy. It is reliable in reasoning.
Make a thin observation layer in two agile sprites
You don’t need a six-month roadmap, just a focus and two short sprints.
Sprint 1 (Weeks 1-3): Basics
Quick registry with version control
The redaction middleware is tied to the policy
Request/Response Log with Trace ID
Basic assessment (PII check, reference presence)
Simple Human in the Loop (HITL) UI
Spirit 2 (Weeks 4-6): Guardrails and KPIs
Offline test set (100–300 real examples)
Facts and policy gateways to safety
A lightweight dashboard tracking slows and costs
Automatic token and latency tracker
In 6 weeks, you’ll have a thin layer that answers 90% of governance and product questions.
MAKE assessment is constant (and boring).
Assessment should not be a one-time event. They should be normal.
Crate test sets from real cases; Refresh 10–20% monthly.
Define clear acceptance criteria jointly by product and risk teams.
Run weekly suite for changes and flow checks for each prompt/model/policy.
Publish a unified scorecard each week covering facts, safety, efficacy and cost.
When evils are part of CI/CD, they cease to be compliance theaters and become operational plus checks.
Apply HMonitoring Oman where it matters
Full automation is neither realistic nor responsible. High-risk or ambiguous cases should increase human assessment.
A low confidence or policy flag response path for experts.
Capture every modification and reason as training data and audit evidence.
Feed reviewer feedback into guidelines and policies for continuous improvement.
At one health-tech firm, this approach reduced false positives by 22% and produced a trainable, compliance-ready dataset in weeks.
cOST control by design, hope not
LLM costs increase non-linearly. Budget won’t save you from architectural whims.
Indicate such precise parts before generating the structure.
Compress and re-contextualize instead of throwing away entire documents.
Memorize tool output with cache repeated queries and TTL.
Track feature latency, throughput and token usage.
When the observation covers token and delay, cost becomes a control variable, not surprisingly.
The 90 Day Playbook
Within 3 months of adopting observable AI principles, enterprises should see:
1–2 Production AI supports with HITL for edge cases
Automated diagnostics suite for pre-defined and overnight runs
The weekly scorecard shared SRE, product and risk
Indicators, policies and results associated with audit-ready marks
At a Fortune 100 client, this structure reduced incident time by 40% and linked product and compliance roadmaps.
Scaling trust through observation
Observable AI is where you turn AI from experience to infrastructure.
With clear telemetry, SLO and human feedback:
Executives gain confidence backed by evidence.
Compliance teams get replayable audit chains.
Engineers iterate quickly and ship safely.
Users are exposed to reliable, explainable AI.
Observability is not an additional layer, it is the foundation of trust at scale.
Sakrishna Kovarapati is a software engineering leader.
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