The Agile Monkeys Labs

Research on Information Retrieval, AI, Machine Learning, and more!

What One Real Agent Taught Us About the Platform We Actually Need

agent platform
agent governance
feedback loops
roi measurement
action visibility
control plane
agent architecture
platform strategy
ai agents
AI
agents
ROI
governance

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.

Mar 19, 2026

What We Learned Moving Retrieval Evals Into the Ingestion Pipeline

rag
retrieval evaluation
ingestion pipeline
chunking
metadata quality
canonical identifiers
hybrid search
agentic retrieval
regression detection
knowledge base management
retrieval-augmented generation
AI
retrieval
ingestion
evaluations
knowledge systems

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…

Mar 19, 2026

How We Use LLMs to Map Unfamiliar Codebases Before Committing to an Estimate

llm
codebase comprehension
technical due diligence
software estimation
software architecture
scope management
engineering estimation
legacy system analysis
integrate vs rebuild
flow mapping
ai-assisted development
scope drift
AI

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.

Mar 19, 2026

How We Dramatically Reduced Cost and Latency in an eCommerce AI Agent

ai engineering
product strategy
llm
latency
architecture

How we reduced token spend and latency in a production eCommerce agent flow, with before-and-after metrics.

Mar 4, 2026

From 229+ Vulnerabilities to Zero: How AI Helped Us Modernise a Legacy Enterprise Application

security
legacy modernisation
AI
Spring
Java
Struts

A story about confronting technical debt, outsmarting false alarms, and building bridges across incompatible worlds — without rewriting everything from scratch.

Mar 2, 2026

Stop ‘Using AI in Your Product.’ Start Building Your Product Out of Agents.

ai
agents
product
evaluation

Why product teams should stop adding AI as a helper and start composing features from agents, tools, prompts, and integrated evaluation.

Feb 26, 2026

You Don’t Need Fine-Tuning an Embedding Model – Building a Summer Embedding Lens for Fashion E-commerce Search

embeddings
search
e-commerce
fine-tuning
personalization

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).

Feb 11, 2026

The State of AI Document Parsing: Practical Findings for Production Teams

ai
document parsing
ocr
benchmarking
production

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.

Dec 11, 2025

Testing in the Era of AI

ai
testing
e2e
automation
stagehand

From ChatGPT experiments to production-ready E2E testing: how Stagehand, Browserbase, and Gemini 2.5 Flash enabled a 70% reduction in test execution time.

Nov 24, 2025

AI Evals Aren’t a Tool. They’re a Mindset.

ai evals
data pipelines
product analytics

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.

Nov 17, 2025

AGI Is Still a Decade Away, And the Real Work Starts Now

AGI
AI agents
model optimization
information retrieval
AI strategy

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.

Nov 13, 2025

Don’t Shrink Your Prompt, Specialize Your Models

fine-tuning
ecommerce
feature-extraction

Putting the mega‑prompt vs micro‑models hypothesis to the test — can specialization really beat size?

Jul 24, 2025

Safely Migrating Production from GPT-4o to GPT-4.1: An Evaluation-Driven Approach

LLM
Evaluation
GPT-4
Production

How we migrated models in production while minimizing risks through a comprehensive multi-level evaluation system.

Jul 24, 2025

Improving Vector‑Search Recall by Fusing Embeddings from Multiple Modalities

information retrieval
e-commerce
research
embeddings

Fusing multiple modalities embeddings have demonstrated consistent improvements (14% over just image embeddings) in fashion e-commerce retrieval.

Jul 24, 2025

Is Apache NiFi the right tool for your AI data pipelines?

apache nifi
data pipelines
ingestion
information 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.

Jul 18, 2025

Don’t Augment Your Generation, Augment Your Retrieval

feature extraction
information retrieval

Feature Augmented Retrieval provides a structured, explicit, and adaptive framework that significantly enhances retrieval accuracy and relevance.

Apr 28, 2025
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