How Agentic AI Works: Planning, Reasoning, and Execution

Understanding how agentic AI actually functions is essential for architects, engineers, and technical leaders making deployment decisions. This guide goes beneath the surface — covering the reasoning loops, memory systems, tool integration, and orchestration patterns that define how modern AI agents operate.

The Agent Loop: The Foundation of Agentic Behavior

Every AI agent operates on a basic loop that repeats until the goal is achieved or the agent determines it cannot proceed:

Observe → Think → Plan → Act → Observe → Think...

This is often called the ReAct loop (Reasoning + Acting), formalized in a 2022 paper from Google Research.

At each iteration:

Observe: The agent receives information about the current state of the world (task description, previous outputs, tool results)
Think: The underlying LLM reasons about what has been done, what still needs to happen, and what the next best action is
Act: The agent executes the chosen action — calling a tool, querying a database, generating text, or asking for human input
Observe the result: The output of the action becomes new input for the next reasoning step

This loop continues until the task is complete, a terminal condition is reached, or the agent escalates to a human.

The Role of the LLM

The large language model (LLM) is the agent's reasoning engine. It handles:

Understanding the goal: Parsing natural language task descriptions into structured plans
Decision-making: At each step, choosing which tool to use, what parameters to pass, and when to stop
Synthesis: Interpreting tool outputs and incorporating them into the ongoing reasoning process
Communication: Generating natural language explanations, summaries, and escalation messages

Modern agents use models like GPT-4, Claude 3.5 Sonnet, or Gemini 1.5 Pro because they excel at following complex instructions, using tools, and maintaining coherent reasoning across long contexts.

Tools: How Agents Interact with the World

Without tools, an LLM can only generate text. Tools give agents the ability to take real actions.

Common agent tools:

Web search: Query search engines for current information
Code execution: Write and run Python, JavaScript, or SQL
Database queries: Read from and write to structured data stores
API calls: Interact with external services (CRM, ERP, email, calendar)
Browser automation: Interact with web interfaces
File operations: Read, write, and process documents
Calculator/math: Perform precise arithmetic

Tools are defined as functions with a name, description, and parameter schema. The LLM selects which tool to use based on its reasoning about what action is needed, then generates the parameters to pass.

Example tool call (simplified):

{
  "tool": "query_erp",
  "parameters": {
    "table": "purchase_orders",
    "filter": "po_number = 'PO-2026-4821'"
  }
}

The agent doesn't know the exact SQL syntax — it describes what it needs in terms the tool understands. The tool handles execution and returns results.

Memory: How Agents Maintain Context

Agents use multiple types of memory to function over extended workflows:

In-Context Memory (Short-Term)

Everything in the current LLM context window — the original task, previous tool calls, their results, and reasoning steps. This is the agent's "working memory." Modern LLMs support 128K–1M token context windows, enabling agents to maintain state across dozens of steps.

External Memory (Long-Term)

For tasks spanning hours or days, agents use external storage:

Vector databases: Semantic search over past experiences and documents (Pinecone, Weaviate, Chroma)
Key-value stores: Fast retrieval of structured information (Redis)
Relational databases: Persistent task state and audit logs

Episodic Memory

Some agents store records of past tasks — what worked, what failed, and why. This enables learning and improvement over time without retraining the underlying model.

Planning Approaches

Basic Prompting (Chain-of-Thought)

The simplest form — the LLM reasons step-by-step within a single prompt. Good for straightforward tasks but doesn't allow for dynamic replanning.

ReAct (Reasoning + Acting)

The agent interleaves reasoning ("I need to check the PO number") with actions (calling the ERP tool). After each action, it observes the result and updates its reasoning. This is the most common pattern in production agents.

Tree-of-Thought

The agent explores multiple possible paths simultaneously, evaluating each before committing to the best approach. Useful for complex decisions with many possible routes to success.

Plan-and-Execute

The agent first generates a complete plan (ordered list of steps), then executes each step in sequence, replanning if a step fails. This produces more predictable, auditable behavior — important for enterprise compliance.

Multi-Agent Architecture

Complex workflows are handled by networks of specialized agents working together.

Common patterns:

Orchestrator + Workers: A coordinator agent breaks down a complex task and assigns subtasks to specialized worker agents (e.g., data extraction agent, verification agent, reporting agent).

Peer-to-Peer: Agents communicate directly with each other, passing outputs as inputs without a central coordinator. Works well for parallel processing.

Supervisor + Reviewers: A primary agent completes a task; a separate reviewer agent checks the output for quality or compliance. Human review triggered only for edge cases.

Pipeline: Output of Agent A becomes input for Agent B. Common in document processing workflows (extract → validate → enrich → route).

Human-in-the-Loop Design

Agentic AI doesn't mean fully autonomous. Well-designed agents have explicit human oversight points:

Confidence thresholds: If the agent's confidence score for a decision falls below a threshold, it escalates rather than proceeding.

Risk-based routing: Certain action types always require human approval (large financial transactions, legal document execution, customer-facing communications above a certain impact).

Exception handling: The agent handles standard cases autonomously; anything outside predefined parameters goes to a human queue with full context.

Audit logging: Every action the agent takes — every tool call, every decision — is logged immutably for review and compliance.

This design gives organizations the efficiency benefits of automation while maintaining the oversight required for high-stakes processes.

Guardrails and Safety

Enterprise agents require explicit safety controls:

Input validation: Sanitize and validate all data before passing to the agent
Output filtering: Check agent outputs for harmful content, PII leakage, or policy violations
Action boundaries: Define exactly which tools, APIs, and data sources the agent can access
Rate limiting: Prevent runaway agents from making thousands of API calls
Rollback mechanisms: Ability to reverse agent actions when errors are detected

The EU AI Act requires that high-risk AI systems (credit decisions, HR screening, law enforcement) maintain full audit logs, enable human override, and inform users when AI is involved in consequential decisions.

A Production Example: Invoice Processing Agent

Here's how all these components come together in a real deployment:

Goal: Process an incoming invoice from email to approved status.

Step 1 — Observe: Agent receives email with PDF attachment.

Step 2 — Think: "I need to extract invoice data, verify against PO, check approval rules."

Step 3 — Act: Call vision AI tool to extract fields from PDF (vendor, amount, PO number, line items).

Step 4 — Observe: Extracted data returned. PO number: PO-2026-4821.

Step 5 — Think: "I need to verify PO exists and amounts match."

Step 6 — Act: Query ERP for PO-2026-4821.

Step 7 — Observe: PO found. Approved amount: $48,200. Invoice amount: $48,200. Match.

Step 8 — Think: "Three-way match successful. Check approval threshold."

Step 9 — Act: Query approval rules. Amounts under $50,000 auto-approve.

Step 10 — Act: Update ERP status to "Approved". Send confirmation email to vendor. Log action to audit trail.

Complete. Total time: 90 seconds. No human involved.

If the amounts hadn't matched, the agent would have drafted an exception report, populated the AP manager's queue with full context, and paused pending human decision.

Conclusion

Agentic AI works by combining LLM reasoning with tool access, memory management, and structured planning — all operating in a loop until the goal is achieved. Understanding these components helps engineering teams design effective agents and gives business leaders realistic expectations about what autonomous AI can and cannot do.

The key insight: agentic AI is not magic. It's a well-designed software system that uses an LLM to make decisions in a controlled, auditable, goal-directed loop.