Systems | Development | Analytics | API | Testing

How to Cut Your AI Agent Costs in Half

Every AI agent starts every session with zero memory. No context, no history, just a blank slate that has to rediscover its environment from scratch, and that costs you real time and money. In this episode of Inside the Stack, Jase Lindgren, Principal P4 User Advocate at Perforce, shows a simple fix: instead of re-prompting your agent every time, capture what it learns in a markdown file, then go one step further and turn that into a custom script. The result, tested across 15 runs: lower cost, faster execution, and better accuracy at catching real issues.

Shift Left Vs Shift Right Testing: Key Differences & Use Cases (2026)

Shift left vs shift right testing isn’t really a debate. Teams that treat it as one end up with gaps that neither approach covers alone. Shift left catches defects before they reach production. Shift right learns from what happens after they do. The teams shipping most reliably in 2026 don’t choose between them. They run both and build a feedback loop where each approach makes the other more effective.

Self-Healing Data Pipelines: The Complete Guide to How AI Agents Fix Failures Automatically

Data engineers spend a median of 44% of their time firefighting pipeline failures instead of building new features. When a schema change breaks downstream workflows or data quality issues cascade through systems, traditional pipelines require manual debugging that can take hours or even days to resolve. Self-healing data pipelines powered by AI agents are changing this reality by autonomously detecting failures, diagnosing root causes, and executing repairs without human intervention.

Agentic Data Integration, Explained: From Static Pipelines to Autonomous Data Flows

Your data team got paged at 3 AM. Again. A schema change in your CRM broke the downstream pipeline, analytics dashboards are showing stale data, and the executive team needs accurate numbers for tomorrow's board meeting. This scenario plays out daily at organizations worldwide. It explains why data engineers spend 44% of their time on pipeline maintenance rather than building new capabilities. Agentic data integration represents a fundamental shift from reactive firefighting to proactive autonomy.

Schema Drift: Why It Breaks Pipelines and How AI Agents Fix It Automatically

Your data pipeline worked fine yesterday. Today, a source system added three new columns to a critical table, and now your entire analytics workflow is broken. This scenario, known as schema drift, is one of the most frustrating challenges data teams face when managing their data pipeline infrastructure. The good news? AI agents can now detect and resolve these issues automatically, eliminating the 3 AM fire drills that have plagued data engineers for years.

Meetup - AI Lakehouse

Watch deep-dive presentations and live demos from industry experts as they unpack next-generation data frameworks built from the ground up for multimodal AI, autonomous data agents, and cross-cloud architectures. Key Technical Highlights Inside: AI Lakehouse Meetup - Bay Area 15th July 2026 Cloudera SanJose Office A must-watch technical guide for data engineers, platform architects, and MLOps teams!

9 Best Agentic AI Data Quality Tools in 2026

Bad data doesn't announce itself. It flows silently through your data pipeline, lands in your dashboards, and feeds your AI models until someone downstream notices the numbers don't add up. By then, the damage is done: a flawed forecast, a miscalibrated model, a compliance gap you didn't see coming. For data engineers and analytics managers, this is a significant operational risk.

Agentic Data Management: What It Is and Which Tools Deliver It

Data engineering teams often spend a substantial portion of their time maintaining pipelines instead of building new data products, particularly as environments become more complex. Many organizations still struggle with stale, inconsistent, or low-quality data, leading to delayed or less reliable decision-making. Traditional data management tools alert you to problems but leave the fixing to human hands.

Connect Your SAP & Non-SAP Data to SAP Business Data Cloud for Unified Enterprise Analytics

You’ve invested in SAP Business Data Cloud to unify your analytics. Your SAP data flows into its’s semantic models and Joule. But your Oracle financials, Microsoft Dynamics operations, and Workday HR data still sit isolated—separate analytics tools, separate metrics, separate dashboards. The result is an incomplete picture. Your analytics reflect part of how your business runs, not all of it.

API testing for agent-driven code: SmartBear ReadyAPI MCP for Claude, Copilot, Cursor, and compatible agents

Key takeaway: When code moves at AI speed and testing stays manual, the widening gap between a code change and its test coverage becomes your biggest quality risk. SmartBear ReadyAPI MCP reduces that gap by letting you generate, run, and heal API tests in plain language, right inside the Claude, Copilot, or Cursor session that wrote the code.