When Agent Skills Evolve: The Need for a Layer of Control
What we learnt from building Rhizomatic, a human-in-the-loop proof of concept for reviewing, versioning and rolling back changes to reusable agent skills.
The Lab
Our experiments and insights from tinkering at the frontier of AI
Guardrails in the Wild: Closing the Retraining Loop for LionGuard 2
Operationalising MLOps with production feedback to continuously evaluate, retrain, and improve our guardrails.
What is a Mixture-of-Experts model, really?
An experiment reveals how different layers shape what a model understands, retrieves and ultimately generates.
Agent Wars, Episode 2: The Brain Wars
On shared memory systems for organisations and the complexities around them
Building an automated Evals workflow that works (and open-sourcing it)
How we built Kaleidoscope: A structured workflow for realistic, scalable, and human-aligned contextual AI evaluations.
The Road Under the Harness
Previously I wrote about building a harness for yourself. This one is about the environment you're building in, and why at enterprise scale, if the platform underneath doesn't exist, individual wins have nowhere to accumulate.
Scaling the Pentesting Team with AI
Engineering Multi-Agent Architectures for Autonomous Penetration Testing.
Harnessing the harness
On building your own multi-agent orchestrator, and why owning the infrastructure around AI matters.
Video Generation Landscape Analysis: The Road to Informative Video
We tested 2026 SOTA models and found a "usability gap".
Yes, you’re absolutely right… Right? A mini survey on LLM sycophancy
Ever spoken to an AI and felt like it was responding with insincere praise?
MetaEvaluator: Systematically Evaluate Your LLM Judges
Measure how well your app is performing and more importantly where it's failing.
Building for Agentic AI - Agent SDKs & Design Patterns
The true value of AI agents lies in loops and self-correction rather than raw reasoning power.
A deeper look into using MCP in the enterprise
A universal "USB-C" for AI?
Benchmarking GPT-5 & GPT-OSS: A Responsible AI Approach
Evaluating dimensions often overlooked by traditional benchmarks.
(Part 2) LLM Safety Alignment for the Singapore Context using Supervised Fine-tuning and RLHF-based Methods
Safety must be "baked in".
(Part 1) LLM Safety Alignment for the Singapore Context using Supervised Fine-tuning and RLHF-based Methods
The process of "teaching" models to be safe
Eliciting Toxic Singlish from r1
A red-teaming exercise that proves even "reasoning" models can be coaxed.