// METHODOLOGY
How the State of Agent Engineering dataset was built
Everything needed to reproduce the 119-posting, 32-employer preliminary snapshot. Same compliance-first approach as the FDE study: only sources whose own terms permit collection, extracted facts and link-backs rather than republished text, and a deterministic coding rubric anyone can re-run.
1. Sources, and why only these three
Data came from exactly three public, unauthenticated ATS job-board APIs, chosen because each operator's own documentation permits fetching public postings:
- Lever Postings API - Lever's own docs state published jobs "may be scraped by third parties."
- Greenhouse Job Board API - "Job Board data is publicly available, so authentication is not required for any GET endpoints."
- Ashby Public Job Posting API - a documented public, no-auth endpoint for currently published postings.
Deliberately excludedbecause their terms prohibit collection: LinkedIn, Indeed, Wellfound/AngelList, YC Work-at-a-Startup, Google for Jobs, and any re-scraped "open" dataset that launders one of those. Lever's robots signalai-train=no is honored: this data is for statistical/reference analysis, never model training.
2. Collection
A hand-curated seed list of roughly 170 mostly US, venture-backed, AI-native and infrastructure companies, extending the seed list used for the FDE study with AI-agent-tooling and applied-AI companies (model labs, agent-framework vendors, evals platforms, LLM-infra startups). Each company's board slug was resolved from its public careers URL, then its one public JSON endpoint was fetched once per ATS, rate-limited to no more than one request per second, with an identified User-Agent. No login, no paywall bypass, no IP rotation. 504 requests were made against 507 candidate (company, ATS) pairs; 102 boards returned a non-empty public job list. The full per-board probe log ships alongside the dataset.
3. Inclusion and exclusion
Included if the posting was live and its title matched the agent/AI-engineer-family lexicon: AI Engineer, Applied AI Engineer, Agent Engineer, Forward-Deployed AI Engineer, Prompt Engineer, or an evals/AI-safety specialist title.
Excluded: generic Software Engineer or ML Engineer postings with no AI-agent-family title match, pure data-science/research-scientist roles, internships, expired postings, and duplicate cross-posts. Deduplication is keyed on canonical employer name (not raw board slug) plus normalized title and location, so a company listing the same role under two slug aliases or on two ATS platforms is counted once, not twice.
4. The agent-signal coding rubric
Unlike the FDE study's five-axis authenticity score, this study codes four independent binary signals per posting, each keyed to a specific regex-matched vocabulary and auditable back to the text span that triggered it:
| Signal | Triggered by |
|---|---|
| Agent framework / agentic pattern | LangChain, LangGraph, CrewAI, AutoGen, AutoGPT, LlamaIndex, “agentic,” “multi-agent,” tool use, function calling, orchestration |
| Production coding | language/stack requirements consistent with shipping production software, not aptitude-only screening |
| Evals / red-teaming / guardrails | “evals,” “red-team,” “guardrails,” safety-review language |
| Autonomy / human-in-the-loop | “autonomy,” “human-in-the-loop,” “escalation,” oversight language |
LLM-stack mentions (RAG, OpenAI API, Anthropic/Claude, vector databases) and programming languages are coded the same way, as independent keyword matches, not a composite score. All coding is deterministic and temperature-0 - the same input text always produces the same output - and not human-adjudicated for every row, so spot-checking is recommended.
5. What gets published (and what doesn't)
Published: aggregate statistics, a per-posting fact table of extracted attributes, and a link back to each live posting. Notpublished: verbatim job-description prose (that is the employer's copyright), and anything from a prohibited source. Extracted facts plus a link are citable without republishing anyone's creative text.
6. Limitations
This is agent-engineering hiring among the AI-native and infrastructure companies in the seed list, on public ATS boards, not the whole market. At 119 postings and 32employers, this round 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. It is a point-in-time snapshot (open roles, not hires), so trend claims need a second edition. This corpus is never blended with the 519-posting FDE dataset at /state-of-fde- different title family, different collection round. Full limitations are listed on the report.