Repetitive, but not simple
Triage, classification, reconciliation, first-pass research. Too variable for a rules engine, too frequent to keep doing by hand.
01 · AI Agent Development
Agents that do the work, not just talk about it
An agent is only useful once it can reach the systems where work happens. Yours gets real tool access, a scoped identity, and behaviour you can measure.
Agents need somewhere to act. If the systems involved have no API, no database access and no export, opening that door is the first piece of work, and you will hear that before you commit to an agent.
Goal, reason, tool, permission, action
01What it is
What this is, concretely
An agent is a model given a job, a set of tools, and a boundary. The model works out what to do next. The tools let it read a record, call an API, write a file, open a ticket. The boundary decides what it may touch, what it must confirm, and what it can never do alone.
Almost all of the engineering is in the second and third part. Choosing a model is a weekend decision. Getting an agent to read the right data, act through the right interface, refuse the right requests, and hand back to a person at the right moment is the work, and it is the difference between something load-bearing and something quietly abandoned.
02Where it fits
The work an agent is genuinely good at.
Stranded between two systems
Someone reads one screen and retypes into another. An agent with access to both removes the copying and the transcription errors that come with it.
Arriving faster than people can absorb
Requests, documents and tickets growing faster than headcount. The predictable share gets handled, the rest escalates with its reasoning attached.
03Approach
How we build one that survives contact with production.
- 01
Tools before prompts
The tool surface is defined first: what can be read, what can be changed, what must be asked about. A tool the agent cannot misuse beats an instruction telling it not to.
- 02
Permissions it inherits
Agents act with a scoped identity, never a shared admin key. Access is granted per tool and per record set, so the blast radius of a bad decision is bounded by design.
- 03
Evaluations from real cases
The evaluation set comes from your actual inputs, awkward ones included. Without it, 'the new model seems better' is a feeling rather than a result.
- 04
A run record you can read
Each run stores its inputs, the tools called, the arguments, the results, and the final action. Why it did that has an answer, not a reconstruction.
04Examples
Systems of this shape
Build patterns and capabilities, not client projects.
- Internal research agent
- Searches internal documents and approved sources, and cites the exact passage. Says it does not know rather than filling the gap.
- Document-processing agent
- Reads incoming contracts, invoices or forms, extracts the fields, validates them against your rules, and routes anything ambiguous to a person.
- Operations workflow agent
- Watches a queue, gathers the context a person would gather, then acts inside its permissions or presents the step for approval.
- Orchestrated agent group
- Narrow agents under a supervisor that decides which to call, keeps a shared record, and holds the budget and stop conditions.
05In production
Running it responsibly
Agents need somewhere to act. If the systems involved have no API, no database access and no export, opening that door is the first piece of work, and you will hear that before you commit to an agent.
It can be turned off
Kill switches per tool and per agent, plus a documented manual fallback for the work it took over.
It knows when to stop
Budgets on steps, tokens, spend and wall-clock time, with hard stop conditions on the actions that matter.
It escalates well
When it hands work back it hands back the context too: what it tried, what it found, what it was unsure about.
It is measured after launch
Success rate, escalation rate, cost per task and latency tracked from day one, so drift shows up as a number rather than a complaint.
06Where and next
Where we do this work
Most of this work runs for businesses in the UAE and Saudi Arabia. We also take on selected projects in the United States and Europe, where the systems an agent has to reach usually look much the same.
Start a projectThe other two lines
- Custom AI ApplicationsProduction software where AI is a working part of the product, not a chat box bolted on the side.
- Managed AI SupportEngineering operations for AI already in production: evaluation, monitoring, and the change that keeps it correct.
- All three, comparedWhat each line is, when it fits, and what it needs from you.
Is there a queue someone works through every morning?
That is usually where the first agent goes. Bring the queue, the systems it touches and the exceptions everyone knows about, and we will tell you whether an agent belongs in it.