20 Must‑Know Agent Engineering Concepts for 2026 (Runtime Mechanisms)

Agent (intelligent agent) : Unlike a simple chatbot, an agent can perceive, reason, act, and continuously adjust its next step until a goal is reached, making it suitable for complex tasks such as bug fixing that require multiple tool‑using iterations.

Harness (runtime framework) : The harness is the system shell that turns an LLM engine into a functional agent. It governs loops, tool usage, permissions, retries, context handling, persistent memory, result verification, and human review. In other words, Agent = LLM + Harness, and differing harnesses explain why agents using the same model behave differently.

Execution Model : Determines how an agent thinks and acts each round. The most common patterns are ReAct (Reason → Act → Observe → Reason…) and Plan‑then‑Execute (plan the whole task first, then follow the plan, adjusting only when necessary). Complex engineering often combines both: a high‑level plan guides overall progress while ReAct handles fine‑grained steps.

Loop Engineering : Extends execution across multiple rounds, allowing the agent to autonomously drive a long‑running task without human intervention. It defines trigger mechanisms, verification logic, state tracking, and stop conditions, essentially turning the agent into a self‑sustaining production line.

Agent State : Captures what the agent knows at runtime—its progress, short‑term context (prompts, tool results, rules), and long‑term resources (files, databases, API results). Proper state management enables checkpointing, persistent storage, and the ability to resume or replay tasks.

Context Engineering : Focuses on what information the LLM sees each round. It stresses delivering the right data at the right time in a structured form, rather than overwhelming the model with raw, unfiltered content.

Context Rot (context degradation) : Even with large windows (up to 1 M tokens), models can lose focus when context becomes noisy. Engineers must keep context concise, prioritize essential information, and regularly prune or compress outdated entries.

Prompt Caching : Stores stable prefixes (system prompts, tool definitions, rules) so they are not recomputed each turn, dramatically lowering token cost and latency for long conversations or repetitive tasks.

Ontology (business semantics) : Provides a formal model of enterprise concepts (entities, attributes, relationships, constraints) so the agent can interpret domain‑specific terms consistently across systems, enabling rule extraction and reusable reasoning.

Live Retrieval : Gives the agent a continuous connection to external, up‑to‑date knowledge sources, ensuring decisions are based on current facts rather than stale context. Implementations include RAG, vector search, graph queries, or any real‑time data fetch from databases or APIs.

These ten concepts form the foundation of the runtime mechanism side of agent engineering; the next article will explore system‑level capabilities such as tool integration, memory, collaboration, and governance.