We define what the agent may decide alone and what it must escalate before any implementation starts
That document is what your security team reads, and it is what stops scope creep when someone asks whether the agent could also issue refunds
Tools are typed and scoped, so the model cannot invent an endpoint it was never given
Long tasks break into steps that checkpoint, so a timeout resumes rather than restarting a payment
Anything the agent cannot resolve stops and asks — an agent that stops and asks is working correctly
Most of the engineering in these projects goes here, not into the prompt
Every change runs against an evaluation set built from your real tasks
When someone edits a prompt and the score drops, we see it before your users do
You keep the harness at handover, so your team can extend it without us
Froggik
"DESH Team maintained effective communication throughout the project."
Thanks to DESH Team's work, the client saw increased product recognition within the cryptocurrency community. The team managed the...
Viktoriia Bernatska
Co-Founder
ChainCrafters
"I liked their corporate policy and how they turned to customers and their wishes."
DESH Team delivered the project on time, effectively improving the site's UX and flow. The team took the time to understand the cl...
Kolya Vovkun
CEO, Founder
Dropshipping
"I really like how they treat their clients."
DESH Team successfully completed all deliverables; the branding was a great fit for the client's company, and the website was done...
Tetyana Yarchak
CEO
A chatbot answers. An agent acts: it calls your systems, changes state and finishes a task. That difference lives in engineering rather than in the model — permissions, retries, validation and audit are what separate the two, and they are most of the work.
Yes, with scoped credentials, a typed tool layer and hard limits on side effects. High-impact actions go through human approval until the evaluation data justifies removing the step, and that decision is yours to make with numbers in front of you.
Bounded tools rather than open-ended access, validation on inputs and outputs, spend and step caps, and an evaluation set that catches regressions before release. The escalation path is defined first, so stopping and asking is designed behaviour rather than a failure.
Whichever wins on your evaluation set. We build model-agnostic, so switching is a configuration change. In practice we route routine steps to a cheaper model and the hard ones to a stronger one, which cuts cost without a difference users can detect.
Source code, the tool definitions, the evaluation harness with its task set, deployment configuration and a runbook. Your team can add tools and extend the agent without us. That is the intended outcome.
