Designing Agentic AI: ReAct to Plan-and-Execute

Agentic AI is the shift from "ask a question, get an answer" to "give a goal, let the system figure it out." Instead of a single LLM call, agentic systems involve loops of reasoning, tool use, and decision-making.

But not all agent architectures are equal. Choosing the right pattern depends on your use case.

Pattern 1: ReAct (Reason + Act)

The simplest agentic pattern. The LLM alternates between thinking and acting in a loop:

Thought: I need to find the pump specifications
Action: search_database("pump P-101 specs")
Observation: Found specs - rated flow: 500 GPM, max pressure: 150 PSI
Thought: Now I need to check if current readings exceed limits
Action: get_sensor_data("P-101", "pressure")
Observation: Current pressure: 145 PSI
Thought: Pressure is approaching the limit. I should flag this.
Answer: Pump P-101 is operating at 97% of max pressure rating...

Best for: Simple tool-use scenarios, Q&A with data retrieval, straightforward tasks.

Limitations: Can get stuck in loops, no upfront planning, poor at multi-step tasks requiring coordination.

Pattern 2: Plan-and-Execute

The system first creates a plan, then executes each step:

Plan:
1. Retrieve equipment specifications for P-101
2. Pull current sensor readings
3. Compare against operational limits
4. Check maintenance history
5. Generate risk assessment

Executing step 1...
Executing step 2...
[continues through plan]

Best for: Complex multi-step tasks, workflows requiring specific ordering, tasks where you want transparency into the process.

Limitations: Rigid plans may not adapt well to unexpected results. Replanning adds latency.

Pattern 3: Multi-Agent Architecture

Specialized agents collaborate, each handling a specific domain or task type:

• Judge Agent: Routes queries to the right specialist
• Data Agent: Handles database queries and data retrieval
• Analysis Agent: Performs calculations and comparisons
• Response Agent: Synthesizes findings into coherent answers

Best for: Complex enterprise systems, diverse task types, systems requiring separation of concerns.

Limitations: More complex to build and debug. Inter-agent communication adds overhead.

Choosing the Right Pattern

Factor	ReAct	Plan-and-Execute	Multi-Agent
Complexity	Low	Medium	High
Task types	Simple	Sequential	Diverse
Transparency	Medium	High	Medium
Reliability	Lower	Higher	Highest
Latency	Low	Medium	Higher
Build effort	Days	Weeks	Months

My Take

For most production systems, I start with Plan-and-Execute and graduate to multi-agent when the task diversity demands it. ReAct is great for prototyping but often too brittle for production.

The key insight: the agent architecture should match the complexity of the problem domain, not the other way around. Don't build a multi-agent system when a simple ReAct loop will do.