Artificial intelligence
Agentic harness
The demo is the easy part. What decides whether an agent survives contact with your business is everything around it: what it is allowed to touch, how you know it is still behaving, and how quickly you can pull it back when it is not.
Who this is for
- Teams whose first pilot worked brilliantly for a fortnight and then did something strange
- Anyone about to let an agent write to a live system rather than just read from one
- Companies who need to show an auditor or a customer how a decision was reached
- Engineering leads who cannot tell whether last week's model change made things better or worse
What we actually do
- Tool definitions with real permissions, so an agent can read the ledger without being able to edit it
- Evaluation sets built from your own cases, run on every prompt, model or tool change, with results you can compare
- Tracing of every step: what the agent saw, which tool it called, what came back, and why it stopped
- Cost and latency budgets per task, with alerts before a runaway loop shows up on an invoice
- Approval gates on anything irreversible, and a rollback path that does not involve a database restore
- Regression checks when a provider ships a new model version, which now happens monthly
- A dashboard your team reads, not a log file nobody opens
How an engagement runs
Usually a four to six week engagement wrapped around an agent you already have, ending with an evaluation suite your team owns and runs. After that it becomes part of ongoing support, because this layer moves fast and a harness left alone for a year is no longer telling you the truth.
Related
Often paired with
AI agents
Software that finishes a task instead of answering a question.
Document processing
Messy documents in, clean structured data out, in the languages your paperwork is written in.
Private AI infrastructure
Your own AI hardware, configured properly, so the data stays in the building and the bill stops growing.