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From BI Dashboards to Agentic Ops: Where Not to Use LLMs

3 min read
BI Dashboards to Agentic Ops
BI Dashboards to Agentic Ops

A practical “don’t use an LLM here” list, a decision test, and safer patterns that save cost and headaches.

TL;DR

LLMs shine in unstructured and exception-handling work with a human in the loop. They are a poor fit for deterministic joins, canonical metrics, and anything that must be identical every time. Start with a decision test, then choose a simpler pattern if the job is deterministic.

The “don’t use LLM here” list

Avoid LLMs when the task is:

  • Deterministic ETL: Row-level transforms, schema validation, type coercion
    Use: SQL/DBT/ETL with tests
  • Canonical metrics: Revenue, churn, MRR, regulatory reports
    Use: Well-defined models, versioned logic, peer review
  • Static KPI dashboards: Fixed filters, fixed drill paths
    Use: Cached queries or materialized views
  • Access control decisions: Who can see which records
    Use: Rules/policy engine (OPA/Cedar-style), not generative output
  • Bulk PII handling: Masking/redaction at scale
    Use: Deterministic redaction libs and column policies
  • Anything that must match exactly: Invoices, tax fields, compliance forms
    Use: Templates and strict validators

The 5-question decision test

If you answer yes to any of these, try a non-LLM pattern first:

  1. Will two runs need to produce the exact same result?
  2. Is there a single correct answer that’s already in a table?
  3. Would a mistake create legal or customer harm?
  4. Can you express the logic in rules or SQL?
  5. Is latency cost-sensitive (pennies per run matter)?

If you answered no across the board and the input is messy (emails, PDFs, notes), an LLM or small agent may help—preferably with review.

A safer architecture for mixed reality

  • Deterministic first: Do all joins, filters, and calculations with SQL/ETL
  • LLM for unstructured bits: Summaries, classification, entity extraction
  • Human-in-the-loop: Confirm anything that creates or changes records
  • Policy envelope: Guardrails for inputs (size, file types) and outputs (allowed actions)
  • Audit trail: Store prompts, parameters, and diffs for each run

This cuts both risk and cost.

Cost reality: tokens are not your only bill

  • Memory/state: Long contexts and “always-on memory” can bloat spend
  • Networking/VPC: Cross-AZ and egress fees sneak into agent workflows
  • Observability: Logs and traces cost money but prevent bigger costs later
  • Retries: Silent retry loops can double your bill—cap them

A hybrid (LLM for messy bits, rules/SQL for the rest) usually wins on TCO.

Patterns that replace LLMs (and work better)

  • Regex + dictionary match for part numbers, SKUs, and short codes
  • Finite state machines for stepwise forms and approvals
  • Policy engine for entitlements and data visibility
  • Heuristic rankers (BM25, embeddings search) before calling an LLM
  • Template + validator for emails, invoices, shipping labels

These are faster, cheaper, and predictable.

Where LLMs do shine in ops

  • Triage: Categorize inbound messages and route to the right queue
  • Summarize exceptions: Compress a messy log into a human-readable brief
  • Suggest next steps: Propose actions for a person to confirm
  • Light extraction: Pull fields from semi-structured text when perfection isn’t required

Pair each with a confirmation step and a rollback.

The “agentic ops” checklist (short, operational)

  • Inputs bounded: Max rows/time, allowed file types, size caps
  • Actions explicit: List exactly what the agent may do (create task, update status)
  • Step cap: Maximum number of tool calls per run
  • PII handling: Mask fields by default, log only what’s needed
  • Explainability: Every run produces a summary and a diff
  • Kill switch: One toggle to stop actions and drop to “suggest only”
Task type Use LLM? Preferred pattern Review needed
Join/clean/validate rows No SQL/ETL with tests Not needed
Static KPI refresh No Materialized views / cache Not needed
Classify inbound emails Maybe Heuristics → LLM fallback Yes
Extract fields from PDFs Maybe Parser → Regex → LLM if needed Yes
Create CRM tasks from notes Yes LLM suggestion → human confirm Yes
Draft internal summary Yes LLM summarization with source links Optional

How to roll this out without friction

  1. Inventory 10 automations and tag them DETERMINISTIC or MESSY
  2. Replace LLMs on deterministic flows with SQL/rules
  3. Add review on messy flows that change data
  4. Cap steps (tool calls per run) and enable logging
  5. Publish a one-page policy: what agents can do, what they must not do

What to measure

  • Cost per successful action (not per token)
  • Error rate on writes and how often people roll back
  • Time-to-answer for common requests
  • Human touches per 100 runs (aim: trending down with safety intact)

Need help choosing where to keep or drop LLMs?

We’ll review your flows and map a keep, replace, add-review plan in one session. Contact Scalevise!