Skill profile · Updated 2026-05-03
Agent Orchestration
Decide when an agent earns its complexity — and build it so it doesn't quietly fail.
What is it?
Agent orchestration covers the engineering decisions around multi-step LLM systems that select tools, plan subtasks, and act. The maturity of the field in 2026 is **knowing when not to use one** — most production wins are still single-call workflows or RAG. The agent earns its complexity when (a) the task requires multi-step decision-making where later steps depend on earlier tool results, (b) tool selection is dynamic across 4+ tools, and (c) user goals vary significantly in natural language. When all three are true, the patterns to know are: tool-list curation (5-10 tools max in the active context), bounded loops with explicit termination, intermediate-step eval, and graceful failure when a tool errors.
Who needs it?
Roles where this skill is explicitly weighted by hiring managers.
Applied GenAI Engineer
Agentic features are now in most product specs. Knowing when to push back ('this is a workflow, not an agent') saves quarters of complexity tax.
AI Solutions Architect
Customers will ask for "an agent". Your job is to translate that into the right pattern — sometimes an agent, often not.
AI Researcher
Agent benchmarks are still mostly broken. You contribute to closing that gap.
AI Security Specialist
Tool calls expand the attack surface. You own prompt-injection-via-retrieved-content threat models and the policies around what tools can do.
Time to proficiency
Realistic benchmarks assuming 8–10 focused hours per week. Adjust for your starting point.
You can explain the difference between a workflow and an agent. You know what ReAct is and can sketch a tool-calling loop.
You have built a working tool-using agent with 3-5 tools, an explicit termination condition, and a basic eval set on multi-step tasks. You handle a tool-error case without the agent looping forever.
You curate a tool list under attention budget, instrument every tool call for cost and success rate, set per-task token budgets, and run regression eval against a held-out trajectory set. You know when to use MCP vs inline tool definitions.
You design multi-agent systems with explicit handoff protocols, evaluate on outcome and trajectory metrics, run controlled rollouts (canary on a fraction of traffic), and ship the boring agentic features that actually work in production.
Prove it with a cert
Complete the AI Agents, then take the AI Agents Fundamentals practice exam on CertQuests to validate your knowledge and add a shareable credential to your profile.
Go to CertQuests