Introduction
A mature Agentic AI workflow can be designed in the following stages.
Stage 1: Define the Outcome
State the high-level goal and the business or practical reason it matters.
Stage 2: Clarify the Request
Use reverse prompting to expose missing requirements, assumptions, taste choices, decision points, and failure modes.
Stage 3: Create the Contract
Define the goal, constraints, output format, and failure conditions.
Stage 4: Load Only Essential Context
Provide the active rules, relevant project information, and current task material. Keep the larger knowledge base retrievable rather than permanently loaded.
Stage 5: Select the Architecture
Decide whether the task needs one agent, several independent agents, a debate room, specialized workers, browser agents, or a review loop.
Stage 6: Route by Capability and Cost
Assign simple work to low-cost models, moderate work to mid-tier models, and difficult reasoning or high-value review to the strongest models.
Stage 7: Execute
Let the agents observe, think, and act through their tools.
Stage 8: Monitor Context and State
Store large outputs in files, summarize tool results, maintain shared status, and avoid allowing the conversation to become the only source of project truth.
Stage 9: Verify
Use tests, prompt-contract checks, fresh reviewers, or resolver agents according to the value and risk of the output.
Stage 10: Learn
Convert durable corrections into properly scoped rules. Convert successful repeatable procedures into skills.
Stage 11: Optimize
Review token usage, model allocation, parallelism, batching opportunities, and whether each component is producing enough value to justify its cost.