Technology Overview

Building effective agents requires combining several capabilities that work straightforwardly in isolation but become complex in combination. The architecture that emerged through 2023-2024 includes planning systems, observation mechanisms, tool access, reasoning loops, safety boundaries, and feedback systems.

Planning distinguishes agents from reactive systems. An agent facing low inventory doesn’t just flag the issue, it generates a multi-step plan: check warehouse inventory, identify alternative suppliers if insufficient, compare pricing and delivery times, initiate purchase orders, update availability dates, and notify relevant teams. The agent constructs this plan based on the situation and its understanding of available actions. The planning isn’t hardcoded; it adapts to circumstances.

Planning systems often use techniques adapted from robotics and game AI. Monte Carlo Tree Search, originally developed for game-playing AIs, allows agents to simulate possible action sequences and evaluate outcomes before executing them. The approach proves valuable for complex decisions with multiple possible paths.

Observation and memory give agents continuity. Unlike stateless API calls, agents can maintain context across interactions. Memory systems typically employ multiple layers: short-term memory for current session context, medium-term memory for recent interactions, and long-term memory encoding learned preferences and outcomes.

These memory systems draw from retrieval augmented generation (RAG) techniques, but agent memory proves more sophisticated, maintaining not just factual information but episodic memory of past actions and outcomes. An agent that tried raising prices during a holiday period and saw increased revenue despite decreased volume learns that this strategy sometimes works, informing future decisions.

Tools give agents the ability to act. Function calling, introduced in 2023, allowed models to recognize when they need tools and how to use them. The breakthrough wasn’t just executing functions but recognizing the need for them and formatting proper calls.

The range of accessible tools expanded rapidly. By 2024, agent frameworks provided access to hundreds of APIs covering inventory, customer data, marketing platforms, shipping providers, and financial systems. An agent handling a customer complaint might check order history, verify shipping status, process refunds, initiate replacements, and schedule returns, each step requiring different tools.

Reasoning loops allow agents to improve their own plans. Rather than executing plans rigidly, agents continuously evaluate outcomes and adjust. Reflection mechanisms enable agents to critique their own actions. After completing a task, the agent assesses: Did this achieve the goal? What worked well? What could be improved? These reflections feed into future planning.

Safety boundaries prevent agents from taking actions outside acceptable parameters. This combines technical constraints (agents can’t access systems they lack permissions for) with logical constraints (agents follow rules about acceptable actions). Implementation varies, some boundaries are enforced through agent design, others through API-level restrictions, still others through learned examples of appropriate and inappropriate behavior.

Feedback and reinforcement let agents improve over time. This isn’t the continuous learning of traditional machine learning models but structured feedback loops where outcomes influence future behavior. When agents successfully resolve issues, those conversation patterns get marked as positive examples. Failed interactions that require human intervention get analyzed to identify failure modes.

The most sophisticated implementations use reinforcement learning from human feedback (RLHF), though most 2024-2025 agent systems rely on simpler mechanisms: tracking success rates, noting when human intervention becomes necessary, and logging edge cases that reveal limitations.