Building Agentic AI Systems: Core Concepts

Using AI during development is very different from using AI at runtime. Many teams say they are “building with AI,” but what they mean is AI-assisted development. True agentic AI systems rely on AI while running.

The key distinction

AI-assisted development

Using AI as a tool while building your application — generating code in AI-based IDEs, debugging with LLM assistance, generating designs or documentation. All of it happens before the app actually runs.

AI-powered runtimes

Systems that involve AI while the app is running — email-responding bots, data-validation agents, research assistants, workflow automation. Here the model helps make decisions and take action as events unfold. This is the space where agentic AI systems operate.

Problem classes well-suited for runtime AI

Not every problem is a good fit. Four problem types work particularly well:

• Generative — creating new things: code, images, or text; summarizing large documents; explaining complex data.
• Reasoning — putting together different pieces of information: comparing legal documents, synthesizing multiple reports, analyzing complex datasets.
• Extraction and transformation — converting COBOL copybooks to JSON, translating languages, extracting structured data from documents.
• Agentic execution — carrying out tasks across systems: booking travel, filling forms, orchestrating workflows, interacting with external APIs.

Challenges in building AI-powered runtimes

• Hallucination and false confidence — plausible but incorrect answers.
• Context limitations — finite context windows.
• Bias and inconsistency — outputs vary across runs.
• Lack of domain grounding — models may violate business rules.
• Semantic drift — instructions misinterpreted along long reasoning chains.
• Cost and latency trade-offs — every invocation carries both.

Context strategies for robust runtimes

Reliable agentic systems need context at different layers. There are four main ways to provide it:

• Prompt engineering (front-loaded context) — fixed instructions in the system prompt: policies, brand voice, applicable business rules.
• Retrieval-augmented generation (inline context) — dynamically retrieving relevant knowledge and injecting it as the request is processed: customer history, pricing rules, internal knowledge bases, relevant passages from large collections.
• Dynamic tool-calling / skills (on-demand context and actions) — invoking tools or APIs during execution to retrieve information or perform actions, well-suited to multi-step workflows and edge cases.
• Memory management — short-term and long-term memory: task state, past interactions, learned preferences.

Strong agents use all four layers together: prompts provide baseline rules, retrieval provides problem-specific context, tools fetch real-time context, and memory keeps things consistent over time.

Memory and knowledge

Agentic systems typically use four types of memory:

• Reasoning memory — the agent's short-term thinking while working: execution plans, intermediate reasoning.
• Working memory — temporary information during a session: current tool outputs, conversation state.
• Episodic memory — lasting knowledge learned from past interactions: user preferences, successful patterns.
• Procedural memory — how to carry out tasks and workflows: task dependencies, workflow progress.

A concrete example

To demonstrate these ideas, I built a simple agentic system that compares current events with stories from fiction to guess what might happen next — running at parallelsignals.com. It needs to extract events from news and fiction, reason about similarities between events, and generate narratives — which together make it a good example of an agentic architecture. It stays reliable through vector retrieval of events using pgvector, episodic memory via a mem0 layer, and LLM-as-judge validation to evaluate correlations.

Implications for practitioners

• Choose the right AI-runtime pattern. Match the problem to generative, reasoning, extraction, or execution. Pick wrong and the system becomes fragile.
• Design for context from day one. Combine prompt templates, retrieval pipelines, tools/skills, and memory. Poor context design is one of the biggest causes of hallucination.
• Build observability and guardrails. Logging and metrics; output validation; alerting and anomaly detection; pre-send filters against prompt injection and data leakage. Observability is now a layer in its own right.
• Invest in the right skillsets. Prompt engineering, retrieval systems, agent orchestration, memory management, and safety engineering.

In many ways, building agentic systems is more like systems engineering than writing prompts.

Agentic AI represents a shift from static assistance to AI making decisions at runtime. Successful systems rest on a few principles: understand the right problem class, design layered context architectures, combine prompts, skills, and memory, and implement strong observability and guardrails.