Claude Agent Developers for Multi-Step, Tool-Using Workflows
Northell places engineers who build Claude agents that run unattended — orchestration, tool schemas, memory, and the evaluation harness that tells you when one has started failing. Every engineer is screened on failure handling and handoff design, not on a demo that works once.
Scope the role
Tell us the product, the stage, and the specific gap — a one-off build, an ongoing engineer, or a whole squad.
Meet 2-3 matched engineers
Shortlisted from our own vetted bench, not a marketplace of unverified profiles.
Start the engagement
Full-time embedded, part-time, or contract-to-hire — begin with a paid trial week before any longer commitment.
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A senior engineer will review this and reply within one business day.
Northell places engineers who build agent systems intended to run without someone watching them. The bench behind this page has shipped 155+ product builds and holds a Clutch Top 20 ranking for Product Designers and Developers. Agent candidates are screened on the parts that decide whether an agent survives production — tool schema design, what happens when a tool call fails, when the agent must hand off to a human, and how you detect degradation before a customer does — rather than on assembling a workflow that succeeds on a clean example. Engagements run full-time embedded, part-time, or contract-to-hire, and every one opens with a paid trial week.
- Orchestration, tool schemas, and memory designed for unattended operation.
- Screened on failure paths and human handoff, not on a happy-path demo.
- Evaluation harnesses so degradation is detected rather than reported by a customer.
- Every engagement opens with a paid trial week before any longer commitment.
Claude Agent Profiles Available Now
Senior Engineer — Customer-Facing Support Agents
Builds agents that handle real customer conversations and know precisely when to stop and escalate.
What you'll build
Retrieval grounded in your actual help content, tool calls into your ticketing and CRM systems, explicit escalation rules, and conversation logging detailed enough to reconstruct why the agent said what it said.
Works with support leadership as much as with engineering.
Why clients choose this profile
Designs the handoff rules first, which is what keeps a support agent from confidently answering something it should have escalated.
Apply to get matched →Engineer — Internal Workflow & Ops Agents
Automates multi-step internal processes that currently consume a team's week and follow written rules.
What you'll build
Agents that read from and write to internal systems, structured outputs downstream code can rely on, approval steps for anything consequential, and a replay path so a failed run can be resumed rather than restarted.
Embeds with the operations team whose process is being automated.
Why clients choose this profile
Automates the process that exists rather than an idealized version, which is why these deployments actually get used.
Apply to get matched →Senior Engineer — Auditable Decision Agents
Builds agents in domains where every automated decision must be explainable months later.
What you'll build
Decision agents with recorded reasoning trails, deterministic guardrails around the model's discretion, mandatory human review on defined thresholds, and audit exports a compliance reviewer can actually read.
Works with product plus compliance stakeholders.
Why clients choose this profile
Treats auditability as an architectural requirement from the start rather than something added after a review flags it.
Apply to get matched →Engineer — Agent Evaluation & Reliability
Joins teams with a working prototype that nobody trusts enough to put in front of customers.
What you'll build
Scenario-based eval suites drawn from real edge cases, regression testing across prompt and model changes, staged rollout from shadow mode to limited traffic, and monitoring that surfaces quality drift rather than just errors.
Works with whoever owns the existing prototype.
Why clients choose this profile
Specializes in the gap between a demo that impresses and a system a team is willing to leave running.
Apply to get matched →What It Costs
We don't publish a blended average rate — a single number hides more than it reveals across seniority, specialization, and region. The ranges above are placeholders until we've tracked enough engagements to publish real medians; ask for current numbers on a scoping call rather than trusting a guess here.
We Turn Most Applicants Away
What we screen for
- A live technical exercise on a real, timeboxed problem — not a take-home someone else could have finished.
- At least one shipped production system they can walk through and explain their own decisions on.
- A code-review / architecture-critique session — how they handle pushback, not just how they present.
- Depth in one stack we place for over shallow 'full-stack everything' claims across a dozen technologies.
- Direct, unassisted communication in a live call — no relay through an account manager during screening.
What we don't do
- We don't forward a resume because it has the right keywords.
- We don't run a single unstructured chat and call it vetted.
- We don't place an engineer we haven't personally worked with or verified.
- We don't quote a rate before we understand the actual scope.
We turn away most applicants before they ever reach a client introduction — we're not publishing an exact rejection rate here until we're tracking it well enough to stand behind the number.
How We Screen Every Claude Agent Engineer
Shipped-work + code review
We check for finished, production systems they've actually shipped — not just tutorial repos or slide decks.
Live technical exercise
A real, timeboxed problem drawn from a past Northell engagement, reviewed by one of our senior engineers.
Culture + communication check
A working session with the actual team they'd join, not only with Northell staff.
Who we're looking for
- Mid-to-staff level, with a track record of shipping production software (exact minimum years: TODO)
- Can walk through at least one system they took from scratch to production
- Comfortable presenting and defending technical decisions live
- Fluent in the core stack for the role, plus its testing and tooling ecosystem
- Experience owning code through code review, deploy, and on-call — not just writing it
- Written and spoken English fluency for client-facing work
- Available for a paid trial week before a longer engagement
- Comfortable working inside an existing codebase, not only greenfield builds
- Reads and writes tests as a default, not as an afterthought
- No conflicting concurrent full-time engagement, for embedded roles
- References from at least one prior client or employer we can verify directly
How it works
- You describe the role — Northell doesn't ask you to write a job post.
- We shortlist from engineers already vetted, not job-board applicants.
- You interview 2-3 matched profiles, not twenty.
- The engineer starts on a paid trial week before any longer commitment.
Common Hesitations, Answered Directly
What if the hire isn't a fit after we start?
That's what the paid trial week is for — flag it during the trial and we requalify or replace the person before any longer commitment is on the table.
What if we need someone full-time, not part-time?
Any engagement can convert from part-time or contract-to-hire into a full-time embedded model without restarting the vetting process — it's a scope conversation, not a new search.
What if our team is fully remote across time zones?
We match for meaningful working-hours overlap during scoping, not just calendar availability on paper.
What if we're not ready to commit long-term?
Start with the paid trial week. It exists specifically so neither side commits before actually working together.
Work This Bench Has Shipped
What Happens Next?
Even if none of the shortlisted candidates is a fit, you keep the scoping notes and a written recommendation on what to look for next.
Common Questions
What counts as an 'agent' versus a simple LLM feature?
An agent plans, calls tools, evaluates results, and decides its next step across multiple turns without a human driving each one. A simple LLM feature is a single request-response call.
How do you prevent an agent from taking a wrong action?
Scoped tool permissions, confirmation steps for irreversible actions, and evaluation against known failure cases before it ever touches production data.
Can agents run unattended, or do they always need a human watching?
Depends on the task's blast radius. Low-risk, reversible tasks can run unattended with logging; anything with real consequences gets a human checkpoint by design.
What frameworks do you use to build agents?
We build directly on Anthropic's tool-use API and the Claude Agent SDK where it fits, adding orchestration frameworks only when the task genuinely needs them — not by default.
How do you test an agent before it goes live?
Scenario-based evals covering the task's real edge cases, plus a staged rollout — shadow mode, then limited production traffic, then full rollout.
Also Hiring
The State of Production Agent Engineering in 2026
Agents Fail Differently From Software
Conventional software fails loudly — an exception, a failed request, an alert. An agent fails by producing something plausible and wrong, and it will keep doing so at full confidence until someone notices. That difference should drive both how you build and who you hire. The engineers worth placing think first about detection: what does a bad run look like, what would catch it, and what happens automatically when it is caught. Our screening pushes hard on that, because it is the question a demo never asks.
Tool Design Decides More Than Prompt Wording
Most agent reliability problems trace back to tools rather than prompts. A tool that returns ambiguous results, accepts under-specified parameters, or fails silently will defeat any amount of prompt refinement. Well-designed tools have narrow contracts, explicit errors, and are safe to retry. This is ordinary API design applied to a new consumer, which is why strong backend engineers often make strong agent engineers, and why we screen on schema design directly.
The Handoff Rule Is the Product Decision
Deciding when an agent stops and hands to a human is not an implementation detail — it defines what you are actually shipping and what risk you are accepting. Teams that leave it implicit end up with agents making consequential calls nobody authorized. Defining it explicitly, per scenario and with thresholds, turns the deployment into something an operations lead can reason about. We look for engineers who raise this early, because raising it late usually means it was discovered in production.
Without Evals, You Cannot Change Anything Safely
The moment an agent handles real traffic, every prompt tweak and model change becomes a risk you cannot quantify without a scenario suite drawn from genuine edge cases. Teams lacking one freeze — afraid to touch anything — or ship changes blind. Building that harness is unglamorous work that determines whether the system can improve after launch, which is why we treat evaluation experience as a distinct, screenable specialty rather than assuming it comes with agent familiarity.
What Is Changing in Agent Engineering for 2026
The centre of gravity has moved from prototyping to operating: more teams have something that works occasionally and need it to work reliably, unattended, under real traffic. That has raised demand for evaluation, observability, and rollback discipline over demo-building. Auditability requirements are also arriving earlier in regulated domains. We are not attaching an automation-rate or cost-saving figure to any of this — those numbers vary enormously by workflow, and quoting one as if it applied to yours would be the sort of invented figure this page marks TODO everywhere else.
The company-wide numbers on this page come from Northell's own delivered work: 155+ product builds shipped, a Clutch Top 20 ranking for Product Designers & Developers, a Manifest Top 4 Product Design Team distinction, and named client engagements including MeetAlfred, Referrizer, NWCC, SmartJen, and Finixflo. We have not yet published a large-sample rate or engagement-tenure dataset — every field marked TODO on this page is a placeholder awaiting that tracking, not an estimate dressed up as fact.
Agent Production Readiness Checklist
The gates we work through before an agent handles real traffic — tool schema design, failure and retry paths, human handoff rules, and the evals that catch silent degradation.
Got it — thanks!
A senior engineer will review this and reply within one business day.