18 posts tagged with "agentic-ai"

LLM costs in production pipelines scale differently from anything else in your data infrastructure. A poorly architected pipeline that sends every event through GPT-4o can burn through thousands of dollars per day. A well-architected one running the same workload might cost a tenth of that — by routing each task to the model that's just capable enough for the job.

This post covers the cost architecture decisions that keep AI pipelines economically viable at scale.

Every metrics review has the same pattern: someone pulls up the dashboard, sees that revenue is up 8% week-over-week, and then spends 20 minutes writing a sentence explaining why. Then they do it again for conversion rate. Then for churn. Then for CAC.

The numbers are already in your warehouse. The context — seasonality, campaigns, product launches, prior period comparisons — is also already in your warehouse. The gap is the synthesis, and that's exactly what a KPI commentary agent closes.

An AI agent is only as reliable as the data it reasons from. Feed it nulls, duplicates, or stale data and it will produce confident, coherent, and wrong answers — often without any obvious signal that something is off. The LLM doesn't know what it doesn't know.

dbt's testing framework is the right place to enforce data quality before data reaches your agents. This post covers a layered testing strategy that catches the most common failure modes before they become AI failures.

Batch pipelines are sufficient for most analytical workloads. They're not sufficient for AI agents making time-sensitive decisions. An anomaly detection agent that works on yesterday's data misses the incident happening right now. A customer churn agent fed weekly snapshots can't act on a user who disengaged three hours ago.

Real-time streaming closes this gap. With Kafka as the event backbone and Flink for stream processing, your agents can operate on data that is seconds old rather than hours or days.

Every data team has the same Monday morning ritual: someone checks whether last night's pipelines ran cleanly, hunts through logs for failures, and manually compiles a status update for stakeholders. It's important work — and it's entirely automatable.

A data quality reporting agent runs overnight, checks every layer of your pipeline, and delivers a clear, human-readable report before anyone opens their laptop. When something is wrong, the report explains what failed, what downstream models are affected, and what the likely cause is.

The most common failure mode in enterprise AI projects is asking an LLM questions about your business data and getting confidently wrong answers. The model doesn't know your revenue figures, your customer data, or your internal processes — it only knows what it was trained on.

Retrieval-Augmented Generation (RAG) fixes this by giving the model the relevant context it needs at query time, retrieved from your actual data. The surprising part: you probably don't need a new data infrastructure to do it. Your existing warehouse already has the data — you just need the retrieval layer on top.

Month-end close is one of the most labor-intensive rituals in any finance team's calendar. Data analysts spend days pulling figures from ERPs, reconciling discrepancies across systems, and formatting reports that executives will read in five minutes. The underlying work is predictable, rule-based, and repeatable — the exact profile for an AI agent to take over.

This post walks through how to build a monthly close reporting agent that handles the full cycle: data extraction, reconciliation, anomaly flagging, and narrative generation.

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