Hire engineers who have shipped agents
Most agent work fails after launch, not during it. You get the engineers who plan for that part before you sign anything.
What you get
4 things that decide this
- 01Senior engineers only. Deciding what an agent must never do alone is a judgement call, and it is not a junior skill.
- 02Production experience with tool calling, MCP and voice agents, in systems with real users on them.
- 03You interview every engineer before they join your team, and you can say no.
- 04They stay after launch under an agreed service level, or they train your team to take it over.
What an agent engineer actually does here
Barely any of it is prompt writing. The job is deciding where the model gets to decide. Everything else is the structure around that call: tools with narrow arguments, a stop rule, a trace of every call, and a human on the exceptions.
For ZhoopZhoop we built an AI receptionist that answers live calls for a multi-branch repair business. Twilio carries the voice. OpenAI function calling makes the decisions. Outbound agents ring suppliers for parts quotes. For Go4Gr8 we used MCP to give a coaching platform tool access without the keys to everything.
- 01Tool design, including what the agent is not allowed to call.
- 02Evaluation harnesses, so a prompt change becomes a measurable event instead of a hope.
- 03Traces and logging, so you can replay what the agent chose on any past run.
- 04Cost and latency control: caching, model routing, and knowing when a smaller model is correct.
- 05Model migration, before your provider retires the one you launched on.
What we have shipped
22
production systems delivered
4
of them agent or AI automation builds
1
using MCP in production: Go4Gr8
100%
of engineers you interview before they start
Where this skill actually matters
Tools the model cannot misuse
A wide toolbox makes an agent choose by vibe. Narrow arguments and one job per tool are what keep it inside your rules.
Evals before release
A fixed set of scored cases, run on every change. Without one, you learn about a regression from a customer.
Voice and real-time
A caller interrupts and changes their mind. Agents that survive a live phone line are a different build from ones that answer email.
Life after the model
Providers retire models. An engineer who has done a migration keeps the model id in config and the test set ready.
- CallFree. We scope the actual problem.
- MatchWe propose named engineers.
- InterviewYou meet them. You can say no.
- EmbedYour standups, your repo, your board.
- StayService level, or a trained handover.
The Interview node is the one a marketplace skips. An assigned profile is not the same as a person you chose.
Agents these engineers shipped
ZhoopZhoop
AI receptionist and parts procurement for a multi-branch auto repair business.
Read the case study →
Go4Gr8
Custom AI sparring-partner platform for leadership coaching.
Read the case study →
Little Tree Confections
n8n + AI meeting-to-action automation for an artisan bakery.
Read the case study →
“I am extremely happy with the results and would highly recommend Hashlogics to anyone.”
Daniel Khin · CEO, PremiumAudit.io
What they work in
Models and protocols
Build
Run
Integrations we have shipped
Tell us what the agent has to do
A 30-minute call. We will tell you whether this needs an agent, a workflow, or neither.
Against the usual options
Three ways to get this skill. They fail differently.
| Criterion | A marketplace or freelancer | An embedded engineer from us |
|---|---|---|
| Who you get | A profile matched to your brief, often assigned. | Named engineers you interview and can decline. |
| Proof of the work | A rating and a self-written summary. | Named clients and systems you can read about. |
| After launch | The contract ends. So does the knowledge. | A service level, or a documented handover to your team. |
| When the model is retired | Your problem, and usually a surprise. | Planned for, with a test set that proves the replacement. |
| Code ownership | Varies by platform terms. | Yours, from the first commit. |
Questions, answered
01How is this different from hiring a freelancer or using a job board?
You get an engineer backed by a team that has already shipped agents, plus someone to call when they are on leave. A freelancer is one person's judgement with no second opinion, which matters most on the decisions an agent build gets wrong quietly.
02What if the engineer is not the right fit?
You interview them before they start, so most mismatches never begin. If one appears later, tell us and we replace the engineer rather than defending the match.
03Which time zones do they work?
Our engineers commit to a real overlap with your working day, agreed before the engagement starts rather than discovered afterwards. Standups, reviews and incidents happen inside that window.
04Who owns the code they write?
You do, completely, from the first commit. That covers the code, the prompts, the tool definitions and the evaluation sets. Teams forget to ask about that last one until they want to move.
05What does it cost to hire an AI agent developer?
Scoping calls are free, and we quote a fixed price after a diagnostic where we need to go into an existing codebase first. Quoting a number before seeing how many systems the agent touches would be guessing, and the guess is always wrong in the same direction.
06Do we need an agent at all?
Often not, and we will say so on the call. Where the steps are fixed and the input is structured, rules cost less and fail more loudly. Some of our best builds have no agent in them.
