// ORIGINAL DATA · v1
The State of Agent Engineering Hiring, 2026
I pulled 119 agent/AI-engineer job postings from 32 employersusing only public ATS job-board APIs (Greenhouse, Lever, Ashby), coded each one against a fixed rubric, and asked a simple question: what actually separates "agent engineering" from a generic AI/ML job posting? This is a companion to the State of FDE study and a separate corpus from it. Everything here is computed from real fetched data. The dataset and methodology are free.
By Adam Boudjemaa · Snapshot 2026-07-24 · CC BY 4.0 · Extracted facts + link-backs only, no verbatim postings
Headline findings
81.5%
of agent/AI-engineer postings explicitly require an agent framework or agentic pattern
97 of 119 name LangChain, LangGraph, CrewAI, AutoGen, tool use, function calling, or multi-agent orchestration. That's the line between an agent-engineering job and a generic AI/ML one.
89.1%
require production coding
Whatever the title says, the real version of the role is still an engineering job.
35.3%
mention evals, red-teaming, or guardrails as part of the role
42 of 119. A third of postings already treat evaluation as a named responsibility, not an afterthought.
16.0%
of postings are based in France or elsewhere in the EU
19 of 119. Small enough that this is reported as one cut, not split further by country.
What these roles are called
"AI Engineer" is the single largest bucket at 42.9% (51 of 119postings), but a third of the corpus ("Other") uses a title outside the five target families entirely — the label hasn't settled yet.
What actually makes it "agent engineering"
Job titles drift faster than job content. This is the signal that matters: does the posting name an agent framework or agentic pattern at all?
What they require
LLM STACK MENTIONS
TOP LANGUAGES
58%
remote
31.9%
onsite
10.1%
hybrid
Experience floor where stated (n=82): median 5 years, p25 3, p75 6.
The France / EU cut
19 of 119 postings (16%) are based in France or elsewhere in the EU. The sample is too small to split further by country without implying precision it doesn't have, so it's reported as one cut.
Context: the demand signal is not just this dataset
This isn't Adam's data, and it isn't computed from the fact table above — it's cited, not asserted: Skills related to agentic AI (AI agents, agentic systems) grew from 0.06% of US job postings in 2024 to 0.23% in 2025 — a year-over-year increase of more than 280%, about 90,000 postings. Lightcast, “Four Takeaways from the 2026 Stanford AI Index,” 2026.
Get the data
Limitations (read these)
- Sampling frame: public Greenhouse/Lever/Ashby boards only, from a hand-curated seed list of about 170 mostly US, venture-backed, AI-native/infra companies. This is agent-engineering hiring among that population, not the whole market. It under-covers large enterprise on private ATS, APAC, and companies not on these three ATS platforms.
- Preliminary sample size: 119 postings across 32 employers is smaller than the FDE study's 519/57 minimum-viable bar. Read every percentage here as directional, not precise, until a larger round runs.
- Snapshot, not flow: point-in-time open roles are not hires. Trend claims need a second edition.
- Title-gate precision: “AI Engineer” is a broad, fast-drifting title. The gate is deliberately narrower than plain “Machine Learning Engineer” to stay on the agent-engineering family this study targets, which means some genuinely agent-adjacent roles with unusual titles are excluded.
- Derived fields (agent framework, evals, autonomy signals) are coded by a deterministic, temperature-0 keyword rubric, auditable back to text spans; not human-adjudicated for every row.
- Never blended with the FDE study. The 519-posting dataset at /state-of-fde is a different corpus (forward-deployed-engineer title family); no row here is drawn from or merged with it.
Cite this
Boudjemaa, Adam. "State of Agent Engineering Hiring 2026," v1. adam-boudjemaa.com, 2026-07-24. https://adam-boudjemaa.com/state-of-agent-engineering. Data licensed CC BY 4.0.