Agentic AI
Agentic AI
Section titled “Agentic AI”Agentic AI is the design pattern where an AI system does more than answer. It plans, takes actions, observes outcomes, and keeps working toward a goal over multiple steps.
How Agentic AI Differs From Basic Prompting
Section titled “How Agentic AI Differs From Basic Prompting”| Basic prompting | Agentic AI |
|---|---|
| One request, one response | Goal, plan, action, observation, iteration |
| Usually no external action | Often uses tools, APIs, or execution environments |
| Stateless or lightly stateful | Maintains task state and intermediate results |
| Best for direct generation | Best for multi-step workflows and automation |
The Agentic Loop
Section titled “The Agentic Loop”The core loop usually looks like this:
- Receive a goal.
- Build or revise a plan.
- Execute the next action.
- Inspect the result.
- Continue, retry, escalate, or stop.
In stronger systems, the agent also reflects on mistakes, changes its strategy, or delegates subtasks to other components.
What Makes Agentic Systems Useful
Section titled “What Makes Agentic Systems Useful”Agentic AI becomes valuable when a task cannot be solved well with a single answer.
Examples:
- research across several sources
- software tasks that need code, tests, and fixes
- workflow automation that touches multiple systems
- investigation tasks that require retrieval and verification
Common Design Patterns
Section titled “Common Design Patterns”Alternate between reasoning and acting. This works well when the system must adapt to new information after each tool call.
Plan then execute
Section titled “Plan then execute”Build an explicit plan first, then work through it. This is useful when the task can be decomposed up front.
Planner-worker
Section titled “Planner-worker”One component decides the structure of the work and other components execute focused subtasks.
Critic or reviewer loop
Section titled “Critic or reviewer loop”One component produces output while another evaluates and requests refinement.
Where Agentic AI Gets Hard
Section titled “Where Agentic AI Gets Hard”Stronger autonomy introduces stronger failure modes.
Reliability
Section titled “Reliability”The system may select the wrong tool, interpret a result badly, or continue past the right stopping point.
Safety
Section titled “Safety”Any system that can act on the outside world needs permission boundaries and approval rules.
State management
Section titled “State management”Long tasks require good handling of memory, intermediate results, retries, and resumability.
Cost and latency
Section titled “Cost and latency”Multi-step reasoning can become expensive fast. Production designs need limits, budgets, and observability.
When Not To Use It
Section titled “When Not To Use It”Do not use an agent just because the word sounds advanced.
If a workflow can be handled by:
- retrieval plus a normal response,
- a deterministic pipeline,
- or a single well-scoped function call,
then a full agentic loop is often unnecessary overhead.
Practical Architecture Checklist
Section titled “Practical Architecture Checklist”- clear goal input
- explicit tool definitions
- state storage
- retries and fallbacks
- human approval for sensitive actions
- tracing and evaluation