AI Agents

Created: 2026-02-20 10:00
#note

AI agents represent a significant advancement in autonomous systems, utilizing large language models as their core reasoning engines to plan and execute complex, multi-step tasks through strategic tool selection and invocation. Unlike static pipelines, agents dynamically assess situations, adapt strategies, and iterate toward solutions, making them essential for applications requiring flexibility, reasoning, and real-world interaction. Understanding agent architecture and design patterns is critical for developing robust, controllable systems in production environments.

Core Loop

The fundamental operational pattern of AI agents follows a Think → Act → Observe cycle. In this loop, the agent first evaluates the current state and objectives, reasoning about possible actions. It then selects and executes appropriate tools from its available toolkit. Finally, it observes the results, integrating this feedback into its reasoning for the next iteration.

graph LR
    A["Current State"] --> B["Think<br/>LLM Reasoning"]
    B --> C["Choose Tool"]
    C --> D["Act<br/>Execute Tool"]
    D --> E["Observe<br/>Process Result"]
    E --> F{"Goal<br/>Reached?"}
    F -->|No| A
    F -->|Yes| G["Return Result"]
    style B fill:#e1f5ff
    style D fill:#f3e5f5
    style E fill:#e8f5e9

This cycle repeats until the agent determines that the goal has been accomplished or that an unrecoverable error state has been reached.

When to Use Agents

Agents are most valuable when tasks require reasoning, adaptation, and multiple tool invocations. However, they introduce additional complexity and latency compared to simpler approaches.

Characteristic	Simple Pipeline	Agentic Approach
Task complexity	Fixed, linear steps	Dynamic, multi-step reasoning
Adaptation	Predetermined behavior	Real-time decision making
Tool selection	Hard-coded sequence	LLM-driven selection
Latency	Low (few LLM calls)	Higher (multiple cycles)
Explainability	Transparent flow	Reasoning implicit in LLM
Cost	Lower token usage	Higher token consumption
Error recovery	Limited options	Adaptive correction

Choose agents when tasks demand reasoning over multiple steps, when optimal paths are uncertain, or when handling novel situations that require intelligent adaptation.

Key Components

An effective AI agent consists of several critical components. The LLM serves as the reasoning engine, processing context and generating decisions about which tools to invoke and how. Tools represent the agent's action space—APIs, functions, or external services that enable concrete operations in the environment. Memory systems maintain context across iterations, with variants ranging from in-context windows to persistent external storage. State management tracks the agent's progress, current objectives, and accumulated results. Finally, guardrails implement safety constraints, preventing hallucination, unauthorized actions, and scope creep through validation layers and confirmation gates.

Agent Types

ReAct (Reasoning + Acting) agents interleave thought processes with actions, explicitly showing reasoning steps before tool invocation. This transparency aids debugging and aligns agent behavior with human expectations. Plan-and-Execute agents first generate a comprehensive plan, then systematically execute steps, making them effective for well-defined domains. Reflexion agents incorporate explicit feedback loops and error correction, learning from failed attempts to refine strategies. Multi-agent systems deploy specialized agents that collaborate, each with distinct capabilities, enabling more complex problem solving through coordinated effort.

Memory Types

Agents employ distinct memory mechanisms depending on requirements. In-context memory leverages the LLM's context window to maintain conversational history and recent observations—fast but limited by token constraints. External memory stores information in databases or vector stores, enabling access to large knowledge bases and maintaining state across sessions. Episodic memory records specific past experiences and their outcomes, allowing agents to learn from previous attempts. Semantic memory represents general knowledge about domains, concepts, and relationships, providing background understanding that informs decision-making.

Human-in-the-Loop

Production agents benefit significantly from human oversight, particularly for irreversible actions, high-stakes decisions, or novel situations. Confirmation gates require agent actions to be validated before execution, reducing risk of costly errors. Human review is especially critical when agents access sensitive systems, handle financial transactions, or produce public-facing content. Implementing review loops introduces latency but provides essential safeguards, making systems suitable for regulated industries and safety-critical applications. For a detailed examination of this pattern in LLM systems, see Human-In-The-Loop LLM Agents.

Key Failure Modes

Hallucination occurs when agents generate plausible-sounding but inaccurate information, particularly about tool capabilities or environmental constraints. Looping happens when agents repeatedly attempt the same action without progress, typically due to misunderstanding tool outputs or unclear objectives. Scope creep results from vague goals, causing agents to expand task boundaries beyond intent or capabilities. Prompt injection represents a critical security concern wherein malicious input through tools or external sources manipulates agent behavior, potentially bypassing safety measures. Mitigation requires explicit constraints, input validation, careful prompt engineering, and monitoring of agent behavior patterns.

Note: providing coding agents with repository-level context files (AGENTS.md, CLAUDE.md) can actually reduce task success rates while increasing cost, as shown by Gloaguen et al. (2026). Agents become more "thorough" (more exploration, more tool calls) but get distracted from the actual fix -> context files may introduce a form of scope creep at the instruction level.

References

Agentic AI Frameworks, Multi-Agent Systems, MCP Protocol, AI Agent Security, LLM Evaluation, RAG, Context Constraints for AI Agents