Why AI Agents Need Governance Layers
Autonomous AI agents operate without adequate oversight. A governance architecture designed for control, transparency, and accountability is needed.
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Governance of AI agents, verifiability and compliance of autonomous systems.
Where a CLI agent's boundary falls inside an SSH session
A terminal agent launched inside an SSH session works on the server, not the workstation: it inherits that environment. Governance lives in the permissions file, not in the prompt. What it guarantees and what it leaves uncovered.
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Open weights and licences: what DeepSeek-R1 actually releases
DeepSeek-R1 ships with weights under the MIT licence, but the open weights label covers the parameters alone: data, training code and the RL procedure stay out. What that changes for anyone who has to audit the model.
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Recognising an agent before you can stop it
Governing an autonomous agent needs three primitives: identification, diagnosis, intervention. They sit at very different levels of maturity, and the asymmetry is the main operational constraint.
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An LLM on a Raspberry Pi 5: local inference with no network
With the 1-4 billion parameter models released in April 2024 and GGUF quantisation, a Raspberry Pi 5 runs an LLM with no connection. That is a constraint of architecture, not a performance figure: text that never leaves the device is subject to no transfer at all.
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Neural networks and quantum annealing on power grids
Applying graph neural networks and quantum annealing to grid control opens a verifiability problem that accuracy metrics do not cover.
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TinyLlama: a training pipeline third parties can reconstruct
TinyLlama 1.1B releases weights, code, datasets and intermediate checkpoints under Apache 2.0. For governance the value lies in the chain from data to weights, inspectable step by step, regardless of model size.
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GPT-NeoX-20B: what makes a model "open"
On 9 February 2022 EleutherAI released GPT-NeoX-20B under Apache 2.0, with weights, training code and a corpus datasheet. A note on release governance and reproducibility.
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Apple M1 unified memory and local machine learning
The Apple M1 SoC (10 November 2020) keeps CPU, GPU and Neural Engine on the same memory, with no copies between separate spaces. Technical notes on the architecture and on what changes when you decide where a training job runs.
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Supervised classifiers for in-line quality control
On a production line that uses machine learning to catch defects, the result depends more on the labels and on optical stability than on the algorithm. Technical notes on features, models and operational constraints.
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Putting a machine learning model into production
Training a classifier is the easy part. Keeping it in service — serialisation, data drift, retraining, accountability for the decisions — is the bill you pay every day.
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Measuring air quality from a moving vehicle
Mounting air-quality sensors on a vehicle produces data the fixed-station rules cannot interpret. Technical notes on measurement traceability, starting from the RIPEG system.
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Schemas competing for CPU time: the architecture of a cognitive-agent runtime
How a schema-based cognitive architecture turns into a concurrency problem: parallel threads, activation energy and resource allocation among competing units, from Selfridge's Pandemonium to C++ implementations.
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