Hire an Agent Engineer.
LangGraph, Anthropic SDK, Tool-Use.
DeFinitive's AI desk launches in 2026 with agent engineering as a core specialism — engineers shipping production agent products on Anthropic's agent SDK, LangGraph, AutoGPT-style runtimes and proprietary stacks. Built on 200+ Web3 placements since 2021. Submit a brief and we'll come back with a written search plan.
Hiring a agent engineer well in 2026
Agent engineering became a distinct specialism in 2025 once tool-use APIs and structured output reliability crossed production thresholds. Gartner projects 40% of enterprise applications will embed agents by end of 2026 — most teams shipping agents today are doing so without engineers who have done it before. The discipline includes runtime design (turn loops, tool schemas, retry / fallback chains), memory architecture (episodic vs semantic, vector vs graph), and the safety surface unique to agentic systems (prompt injection through tool returns, runaway tool-call cascades, irreversible actions).
Agent engineering overlaps with FDE on customer-facing delivery and with safety engineering on adversarial robustness, but the centre of gravity is different — agent engineers spend their time on framework code, tool schemas and runtime invariants. The strongest candidates have shipped at least one agent product to real users (not internal demos) and have war stories about specific failure modes — runaway tool-call loops, memory drift, prompt injection through retrieved content.
When DeFinitive runs an agent engineer search, our sourcing strategy taps LangGraph and Anthropic SDK contributor networks, AI x crypto convergence engineers building on-chain agent products, and senior generalist engineers who pivoted post-2024. The screen prioritises shipped agent products, framework contributions and the ability to articulate at least three production failure modes with specific mitigations.
What this role typically owns
- ▸Design agent runtimes — turn loops, tool schemas, retry policies, error / fallback chains
- ▸Implement memory systems (episodic, semantic, vector retrieval, graph stores) for multi-turn agents
- ▸Build tool schemas with type-safe contracts and adversarial-aware return-value handling
- ▸Ship and operate agent products in production — observability, eval pipelines, regression suites
- ▸Coordinate with safety + product teams on prompt-injection mitigations and irreversible-action guards
Signals we screen for
Every candidate passes a three-stage screen — technical, portfolio, culture. These are the proof signals that separate strong candidates from credentialed ones.
- ✓Shipped agent products to real users — not internal demos or hackathon projects
- ✓Open-source contributions to LangGraph, Anthropic agent SDK, CrewAI, AutoGPT-style frameworks
- ✓War stories with specific mitigations — runaway loops, prompt injection, memory drift
- ✓Type-safe tool-schema design — Pydantic, Zod, or custom contract systems
- ✓Eval pipeline experience — agent-specific evals, not just LLM evals
Agent compensation in 2026
Agent engineers in 2026 earn $170K (mid) to $260K+ (senior) base salary. Specialists at AI-native crypto firms or frontier-lab agent teams clear $300K base at the principal tier. Total compensation including equity typically adds 30-60%. The premium over generalist ML engineering reflects scarcity of production agent experience.
How the search runs
- 01
Brief (Day 0)
30-minute call with Nathan or the AI desk principal. Role spec, technical bar, compensation structure including equity / token grants.
- 02
Vetted shortlist (Day 3)
3-5 vetted candidates within 72 hours. Each passed our three-stage screen tuned for AI roles. Only 12% of sourced candidates make the shortlist.
- 03
Hire and pay (when they sign)
Pure contingency. You pay nothing until they accept and start. 60-day replacement guarantee.
Agent Engineer hiring FAQ
How is an agent engineer different from an FDE or genAI engineer?
An FDE is customer-embedded — they ship in the customer's stack and own the full deployment lifecycle. A genAI engineer typically ships LLM features in a host product (RAG, summarisation, classification). An agent engineer specifically owns the runtime — tool-use orchestration, memory, multi-turn loops. The skills overlap but the centre of gravity is different. Senior candidates often have done all three at different points.
What frameworks should an agent engineer know?
It depends on your stack. Anthropic's agent SDK is the leading framework for production agents on Claude; LangGraph is the open-source incumbent across multi-provider setups; OpenAI Assistants is the lock-in option on GPT-class models. Senior agent engineers should have shipped at least one production agent on one of these and have opinions about why they'd pick another. We match candidate framework experience to your existing stack.
How will you vet agent engineers?
Same three-stage model that runs our Web3 desk, calibrated for agent work. First: technical screen on the runtime stack — turn loop design, tool schema patterns, retry policies, observability. Second: portfolio review of shipped agent products with at least one specific production failure mode and mitigation. Third: culture fit. Agent engineers who cannot articulate failure modes are demo engineers — the screen filters for production scar tissue.
How much do agent engineers cost?
Mid-level $170K base, senior $215K median, principal at AI-native crypto firms and frontier-lab agent teams clearing $260K-$300K base per public LinkedIn aggregate data. Total comp adds 30-60% via equity / token grants. The market for agent engineers is younger than the market for FDEs or safety engineers, so compensation is rising faster (roughly 25% annually since 2024).
How long will an agent-engineer search take?
Our Web3 desk delivers a vetted shortlist in 72 hours and closes most roles in 4-6 weeks. We are committing to the same operating standard on the AI desk; first AI mandates will calibrate the bands. Most strong agent engineers are actively engaged but the pool is broader than safety engineering, so candidate availability is rarely the bottleneck — calibration on framework + production-experience requirements usually is.
Are agent engineers usually remote?
Public posting data suggests roughly 70% of agent-engineering roles are fully remote — higher than safety engineering because the work is implementation-heavy rather than research-heavy. Senior agent engineers cluster in SF, NYC, London, Berlin and Singapore but most teams accept time-zone-overlapping remote candidates.
Related
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