We build and use AI agents for practical work: checking AI-generated output, monitoring infrastructure, and handling routine operations. Our starting point is the work we do inside Borg Dynamics.
An agent writes release notes. They read well, but contain the wrong attribution and say a check was not done when it was. In our own work, a separate agent using a different model stack caught both errors before the notes reached a customer. This is where independent review helps: checking the facts behind a plausible draft.
When infrastructure behaves differently, someone needs to work out what deserves attention. We use agents for infrastructure monitoring and anomaly detection in our own operations. For a customer workflow, we would start with an agreed set of signals and define what the agent should flag for human review.
Content needs drafting, release records need updating, and discovery work needs follow-through. Vala, our operational agent and chief of staff, does this work inside Borg Dynamics. This page is one example: Vala prepares the content for review, with approval required before changes to the website.
When AI-generated work looks convincing but may be wrong, we can help add an independent review step. Start with a defined output, such as release notes, and check its factual claims against the source material before it is used.
When routine content, records, or discovery work keeps falling behind, we can help turn it into a defined agent workflow. Set the inputs, expected output, and points where a person must approve the next action.
When changes in infrastructure are hard to track, we can help scope an agent workflow to review selected signals and flag items for investigation. Agree the monitoring scope and human response before putting it into use.
Tell us what needs doing, what information is available, and who needs to approve the result. We can assess where an agent could help and what would need to be tested first.
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