Matthew Gribben Blog

Most agentic development today is building the exciting half of the system and hoping the boring half takes care of itself.

Markdown skills, LLM wikis, and 'the model will stay on the rails' approaches feel like progress.

Almost every agentic system conflates two completely different problems: what the model is allowed to *know* for a given purpose, and what it is allowed to *do*.

RAG and 'give the model everything and let it figure it out' are the same mistake at different levels of sophistication.

The only way an agentic system compounds instead of accumulating sludge is if work is decomposed into bounded, auditable units, context is explicitly assembled per unit, execution produces first-class evidence, and only some of that evidence is deliberate…

A D700-specific guide to running llama.cpp with Vulkan on the 2013 Mac Pro: dual 6 GB FirePro cards, Ubuntu, RADV, full GPU offload, cooling, and the traps that make old GCN hardware look slower than it is.

If you’ve spent any time inside OpenClaw, you already know it’s a playground for people who like to wire weird things together and see what happens.

Memory failures in agents are rarely solved by larger context windows.

Most token burn in agent systems is self-inflicted.

Most multi-agent systems fail because teams mix up reasoning, memory, and orchestration.

Once you can simulate Conditional Access decisions, the next step is operational discipline: baselines, versioned expected outcomes, change impact analysis, and regression testing across one tenant or many.

If Conditional Access behaviour depends on sign-in context, client path, device state, service dependencies, and policy interaction, then static review alone is not enough.

Conditional Access feels random not because the engine is unpredictable, but because real access outcomes depend on apps, dependencies, exclusions, scope drift, device state, network context, and session timing outside the visible policy list.

Exploring why breaking down agent behaviors into atomic, verifiable units leads to more reliable and debuggable AI systems.

Conditional Access is not a firewall for Microsoft 365.

Agent memory should not be built from chat transcripts alone.

Practical insights from implementing ZK circuits at scale, covering performance optimization, security considerations, and common pitfalls.

How atomic knowledge generation can create more robust and verifiable AI knowledge bases compared to monolithic embeddings.

Applying consensus protocols and distributed state management to coordinate autonomous AI agents at scale.

Building AI systems with security as a foundational concern, from input validation to output sanitization and model isolation.

Scam calls that arrive wearing a UK number are not magic.

Phone scams are not just a consumer problem.

Sony did not just buy a chat integration.