Orchestrates production-grade LLM-driven agent systems in 2026. Use for agent architecture, control loops, topology choice, tool contracts, memory, approvals, safety boundaries, evals, and observability.
Plans and specifies production-grade agent systems. Use when scoping work, writing implementation plans, defining state and tool contracts, sequencing milestones, and producing clear done criteria for LLM-driven systems.
Designs production-grade AI agent experiences. Use for PRDs, user journeys, interaction models, trust and approval UX, failure handling, provenance, and measurable acceptance criteria for agentic products.
Engineers backend systems for LLM-driven agents. Use when designing APIs, orchestrators, tool runtimes, persistence, jobs, auth, retrieval and memory services, approvals, budgets, and reliability controls.
Database engineering skill for agentic systems. Use for schema design, migrations, query correctness, performance, transactions, multi-tenant boundaries, privacy, backups/rollback, and safe DB operations.
Establishes reliability, safety, and release readiness for LLM-driven agents. Use for eval suites, adversarial testing, observability, budgets, policy gates, prompt-injection defenses, and rollout controls.
Designs and implements frontend experiences for agentic applications. Use for chat and non-chat agent UX, streaming state machines, plan and receipt surfaces, approvals, provenance, failure recovery, and trustworthy interaction design.
Defines product vision and strategic focus for AI agent products. Use for product direction, prioritization, positioning, roadmap choices, user-value framing, and deciding what to cut to achieve an excellent end-to-end agent experience.