Why regulated companies should focus less on baseline agent capability and more on the operating environment that makes enterprise work usable, governed, and measurable.
When AI produces the work but the brand still lives where only humans can read it, identity drifts one degree per cycle. Move the brand to where the AI reads.
A control plane isn’t your starting point. It’s what remains after one real agent has forced you to solve retrieval, permissions, review, action visibility, and outcome tracking.
Most retrieval regressions don’t begin when the user asks a question. They begin earlier, when new content is parsed, chunked, labeled, indexed, and quietly made available to the agent. Once we saw that clearly, we stopped treating retrieval QA as a chat problem and started moving it into the…
This is the point where a supposedly small feature often reveals itself. Sometimes it’s a straightforward extension of existing flows. Sometimes it’s an integration into a partially built capability. And sometimes there’s a genuine architectural mismatch that justifies a rebuild.
How we reduced token spend and latency in a production eCommerce agent flow, with before-and-after metrics.
A story about confronting technical debt, outsmarting false alarms, and building bridges across incompatible worlds — without rewriting everything from scratch.
Why product teams should stop adding AI as a helper and start composing features from agents, tools, prompts, and integrated evaluation.
A step-by-step story of how we nudged our vector search toward beach-ready products without fine tuning the whole model (or re-indexing eleven-thousand SKUs).
64 controlled runs comparing GPT-5.1, Claude Sonnet 4.5, AWS Textract, Azure Document Intelligence, and Google DocAI across passports, certificates, and tax forms.
From ChatGPT experiments to production-ready E2E testing: how Stagehand, Browserbase, and Gemini 2.5 Flash enabled a 70% reduction in test execution time.
Evals is not a UI, or a dashboard, or a platform — it’s about data. Understanding what evaluating really means and building a culture of continuous evaluation.
Highlights and reflections from The Agile Monkeys team discussion on Andrej Karpathy’s interview with Dwarkesh Patel — exploring cognitive cores, autonomous agents, and the practical path to AI integration.
How we migrated models in production while minimizing risks through a comprehensive multi-level evaluation system.
Putting the mega‑prompt vs micro‑models hypothesis to the test — can specialization really beat size?
Fusing multiple modalities embeddings have demonstrated consistent improvements (14% over just image embeddings) in fashion e-commerce retrieval.
Apache NiFi is a powerful visual-coding tool to build data pipelines. We explore its pros and cons and whether it’s the right tool for building your AI data pipelines.
Feature Augmented Retrieval provides a structured, explicit, and adaptive framework that significantly enhances retrieval accuracy and relevance.
