Robert Half is one of the most recognizable names in staffing, but recognition built in a pre-AI world doesn't automatically translate to results in 2026. If you need a senior engineer who ships 3x faster because they've mastered Cursor and Claude Code, Robert Half's generalist model creates a critical gap you'll have to fill yourself.

Robert Half is a mature, globally scaled staffing operation with genuine strengths in speed and coverage. For commodity IT contracting and generalist tech roles, it remains a viable option. For engineering teams that need AI-native developers, the platform's vetting methodology hasn't evolved fast enough to reliably surface that talent, and the burden of identifying it falls back on you.

Robert Half reported $6.53 billion in global revenue in 2023 and operates across more than 400 locations worldwide. That scale is real and shouldn't be dismissed. The company has decades of operational experience, a global contractor network, and an internal cloud-based platform that gives recruiters real-time market intelligence to move quickly on open roles. But here's the structural reality you need to understand: technology staffing at Robert Half sits alongside practices in finance and accounting, legal, marketing and creative, and administrative support. Engineering talent isn't a focused vertical. It's one slice of a very wide pie. When you post a senior backend engineer role with a requirement for AI-tool fluency, you're working with a recruiter who this week may also be filling a paralegal contract and a CFO search. That's not a knock on Robert Half's people. It's a structural observation about where specialized AI-era engineering vetting fits in their business model. The answer is: it doesn't yet.

The Robert Half mobile app lets candidates upload a resume, search roles, submit time reports, and manage pay from their phone. It's a polished experience for contractors managing active engagements. The internal Salesforce-based platform gives their consultants workflow visibility and account data. These are real operational investments. What's absent from the public-facing process: any standardized technical coding tests, pair-programming sessions, or enforced use of AI coding tools as part of candidate vetting. Their matching model is described as AI-supported and recruiter-driven, which in practice means resume-to-job-description alignment, not hands-on evaluation of how a candidate actually builds software in 2026.

This is where Robert Half's model shows its age most clearly. The company's publicly described process is built around resume review, recruiter phone screens, and AI-assisted matching on job requirements. There is no announced workflow requiring candidates to demonstrate competency with GitHub Copilot, Cursor, or Claude Code. There is no enforced coding assessment layer. The practical consequence: if you hire a "senior software engineer" through Robert Half and that engineer has never used an AI coding assistant seriously, the platform has no mechanism to catch that. You won't know until they're on your team and moving at the pace of 2019. For a company that needs to field an AI-augmented engineering team, this creates a concrete risk. You're not just vetting whether someone can code. You're vetting whether someone can code with AI, which is a fundamentally different skill set. An engineer who treats Copilot as autocomplete and ignores Cursor's agent mode isn't delivering the 40-60% productivity gain that elite AI-native engineers are now demonstrating. Robert Half's current process doesn't help you distinguish between the two.

Robert Half's sourcing model is breadth-first. Their job board and app surfaces thousands of roles weekly across all their verticals. Candidates self-select into the pool by uploading a resume and applying. Recruiters then match from that inbound pool combined with their existing contractor database. This is efficient for volume. It's less reliable for signal. When the candidate pool is self-selected and evaluated primarily by recruiter judgment, quality consistency becomes highly dependent on which local office you're working with, which recruiter you draw, and how much attention your role receives relative to higher-margin or easier-to-fill searches. G2 reviewers consistently praise recruiter responsiveness and speed to present candidates. The criticism that appears repeatedly: inconsistent candidate quality and vetting depth that varies by office. That variance is a structural feature of a recruiter-judgment-driven model, not a bug that will be fixed with a software update. Reddit threads in r/recruitinghell characterize Robert Half as useful for quick, short-term tech contracts but "less specialized for advanced software engineering roles compared with niche or engineering-focused platforms." That community-level perception has stayed consistent and reflects the structural reality above.

For certain hiring needs, Robert Half delivers solid results:

Where the quality proposition weakens considerably:

The issue isn't that Robert Half has bad engineers in their network. Some excellent engineers absolutely cycle through staffing agencies. The issue is that the platform's vetting layer isn't designed to sort for AI-era competency, so the good-to-great ratio becomes harder to predict without your own added screening.

This is a genuine Robert Half strength. With 400+ locations, a large existing contractor database, and recruiter teams incentivized to fill roles quickly, Robert Half can surface candidates fast. For a standard IT contractor role, expect candidates within days. Speed matters. But speed-to-candidate and speed-to-right-candidate are different metrics. If your team is running a rigorous second-pass technical evaluation anyway (which you'll need to do if AI-tool fluency matters to you), the first-pass speed advantage from Robert Half compresses.

Candidates report a reasonably smooth application process through the app. Recruiters are generally described as responsive and communicative during active searches. The experience that draws more criticism: the feel that individual recruiters act as independent agents with inconsistent standards, and that the platform's matching algorithms optimize for resume keyword overlap rather than depth of technical capability. Hiring managers report getting candidates who look right on paper but haven't been evaluated hands-on. For candidates who are genuinely AI-native engineers, there's a separate frustration: Robert Half's process offers no pathway to demonstrate that differentiation. An engineer who ships 3x faster using Cursor's agent mode looks identical to a slower engineer in a resume-based screening. The platform doesn't reward or surface that skill.

The fundamental difference between Robert Half and Nextdev is what each platform was built to find. Robert Half was built to staff. Nextdev was built to identify AI-native engineers at a moment when that distinction is the most important variable in engineering hiring. These aren't the same problem.

Where Robert Half requires you to add your own AI-vetting layer on top of their delivery, Nextdev's assessment process is built around exactly that evaluation from the start. Candidates demonstrate how they actually build software: with Cursor, with Claude Code, in real development environments. That's not a feature bolted on as an afterthought. It's the entire point. The teams that will win in the next five years aren't hiring more engineers. They're hiring better engineers who multiply their output with AI. That's the Navy SEAL model: smaller units with more capability per person, deployed on more fronts simultaneously. Robert Half can fill seats. Nextdev finds the engineers who change what's possible per seat.

Robert Half is a reasonable choice if:

Consider alternatives if:

Robert Half isn't a bad company. It's a 2005 solution trying to serve a 2026 problem. The scale, the recruiter network, the geographic coverage: those are real assets for real use cases. But the gap between "recruiter-vetted IT contractor" and "AI-native engineer who multiplies your team's output" is the most consequential gap in engineering hiring right now, and Robert Half's current model doesn't close it. The engineering teams that will build category-defining products over the next five years won't be the largest. They'll be the ones where every engineer is genuinely operating at AI-augmented capacity. Finding those engineers requires a platform built specifically to identify that capability, not a generalist staffing model with an AI-matching layer on the resume inbox. That's the real question to ask any hiring platform in 2026: not "how many candidates can you deliver?" but "how do you know your candidates can actually build software the way the best teams build it today?" Robert Half doesn't yet have a strong answer to that question.