Agentic Workflow
A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
An agentic workflow chains planning, tool use, and self-evaluation in a loop until a goal is reached. The agent decomposes the task, chooses tools, inspects outputs, and decides the next step.
In operations, it powers tasks like research-and-draft, ticket triage, invoice coding, or QA of generated assets. Each step can branch: fetch data, analyze it, propose an answer, validate against rules, then send or escalate.
It fits into systems as a modular runner behind a trigger—API call, form submit, or schedule—returning structured results. The payoff is fewer manual handoffs and faster resolution; the tradeoff is the need for strict guardrails and observability.
Frequently Asked Questions
How do I decide the steps in an agentic workflow?
Define the goal, break it into observable checkpoints, and specify success criteria per step. Limit the toolset to the minimum needed—fetch, analyze, act, validate.
What safeguards should I include?
Schema validation, allow/deny lists, rate limits, max iterations, and circuit breakers. Add human review for high-risk branches like money movement or customer comms.
How do I test an agentic workflow?
Use recorded scenarios with expected outputs, run in shadow mode, and diff agent actions against ground truth. Track failures by category to refine prompts and tools.
Can I mix deterministic logic with agentic steps?
Yes—wrap critical rules in deterministic checks and let the agent handle fuzzy tasks like drafting or classification. Keep writes gated by validations.
How do I monitor performance?
Log per-step latency, error reasons, tool usage, and escalation rate. Set SLOs for end-to-end time and success rate; alert on drift.
What triggers work best?
Webhook events (tickets, leads, invoices), schedules (batch QA), or API calls from other systems. Always capture the triggering payload for replay.
How do I control costs?
Cap iterations, cache retrieval results, choose model tiers by step (cheap for recall, better for reasoning), and short-circuit on confident outcomes.
When should I escalate to a human?
When confidence drops, rules are violated, or outputs fail validation. Route with full context: steps taken, tool outputs, and the proposed action.
How do I version agentic workflows?
Store prompts, tool definitions, and routing logic in version control. Tag releases, and keep a rollback path if metrics degrade.
Agentic AI
An AI approach where models autonomously plan next steps, choose tools, and iterate toward an objective within guardrails.
Agent Handoff
A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
Agentflow
A meta-framework for composing multi-step, tool-using agents with routing, memory, and validation built in.
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