Agent Brain
Prompt + policies that define how the system reasons, chooses tools, and responds.
An agentic workflow is a goal-driven loop where AI systems evaluate context, choose actions, and adapt step by step instead of blindly following static rules.
This pattern shows the core loop: observe context, plan decisions, execute actions, and evaluate outcomes before starting the next pass.
Step 1
Collect context from user input, history, and system signals before deciding.
Step 2
Pick a strategy and sequence of actions based on goals and constraints.
Step 3
Call tools, write outputs, or trigger tasks while keeping state updated.
Step 4
Score outcomes against objectives, then refine the next cycle.
Prompt + policies that define how the system reasons, chooses tools, and responds.
APIs, databases, and services the agent can call when it needs real world actions.
Persistent context so each step builds on prior decisions instead of starting from zero.
Validation, auth checks, rate limits, and fallback paths to keep execution safe.
Example 01
Reads CRM updates, drafts personalized messages, waits for approval rules, and schedules outreach.
Impact: Reduced response lag from 18 hours to 35 minutes.
Example 02
Triages tickets, fetches account context, proposes replies, and escalates edge cases automatically.
Impact: Handled 62% of tier-1 tickets without handoffs.
Example 03
Transforms one source article into social posts, email drafts, and update notes with QA checks.
Impact: Shipped weekly content in one afternoon instead of three days.
Start with one high-friction process, instrument every decision, and scale only after your loop is reliable.