Building an Agentic Agent involves orchestrating several complementary technologies — including large language models, memory systems, planning engines, tool integration layers, and event-driven execution frameworks. These components work together to simulate autonomy, memory, adaptability, and purpose-driven behavior.

⚙️ Core Technology Stack Overview

• LLM (Large Language Model): The reasoning engine — processes natural language, generates responses, plans actions, and performs inference.
• Memory System: Stores past experiences, goals, interactions, and facts using vector databases or custom memory layers.
• Planner/Controller: Generates action plans based on goals; breaks them down into steps (sometimes hierarchically).
• Tool Integration Layer: Enables interaction with external APIs, scripts, environments, and databases.
• Event Loop or Agent Runtime: Keeps the agent “alive” across sessions, manages timing, schedules tasks, and supports reactivity.

🧠 Core Language Model Options

• OpenAI GPT-4 / GPT-4o – Popular, robust reasoning and tool-use capabilities (via function calling).
• Anthropic Claude – Excellent for reflective reasoning and long-context tasks.
• Google Gemini – Great for multimodal and grounded tasks.
• Mistral / Mixtral – Lightweight open-source alternatives with agent use cases.
• LLaMA 3 / Orca / Zephyr – Open models often used for fine-tuning agent behaviors.

🧩 Memory Layer Technologies

• Vector DBs: Pinecone, Weaviate, Chroma, Qdrant — store and retrieve embeddings of past interactions.
• Structured Memory: Use JSON, YAML, or SQL for storing state, goals, timelines, and history.
• Hybrid Systems: Combine vector and relational storage to track semantic and factual knowledge.

🔧 Tool Use & Function Interfaces

• LangChain Tooling: Wraps functions, APIs, and I/O operations as callable tools by the agent.
• OpenAI Function Calling: Natively supports JSON-based tool schemas.
• Serverless MCP (Model Context Protocol): Emerging standard for agent-safe tool access via microservices.
• Browser/Terminal Agents: Use Puppeteer, Playwright, or subprocess runners for web or CLI automation.

🧠 Planning and Control Strategies

• ReAct (Reason + Act): Chain-of-thought prompting with action execution in loops.
• BabyAGI: Loop-based task execution with memory and recursive goal creation.
• Task Decomposition: Agents that break large goals into smaller plans (e.g., hierarchical planners).
• Self-Reflective Agents: Use prompts like “Was this effective?” or “Should I revise my plan?”

🏗️ Frameworks and Libraries

• LangChain: Framework for LLM agents with memory, tool use, and chains.
• CrewAI: Agent collaboration and role-based tasking system.
• OpenAgents: Meta’s multi-agent coordination and execution framework.
• Semantic Kernel (Microsoft): .NET/JS/Java toolkit for agent memory and skills.
• AutoGen (Microsoft): Multi-agent orchestration library based on collaborative AI.

🗃️ Storage & State Persistence

• JSON/YAML: Lightweight persistent store for single-agent state.
• PostgreSQL/SQLite: For structured plans, identities, and agent memories.
• Redis: Fast key-value caching and state tracking for live agents.

🔐 Agent Safety & Governance Tools

• Guardrails.ai / Rebuff.ai: Set boundaries for prompt output and function calls.
• Prompt Injection Defense: Regex filters, role segmentation, and authorization gates.
• Telemetry Dashboards: Monitor agent actions, tool use, and memory reads.

🚀 Full Example Stack (Simple Agent)

• LLM: GPT-4 via OpenAI API
• Memory: Chroma vector DB + JSON plan tracker
• Planning: ReAct-style CoT reasoning
• Tool Use: LangChain Python tools (search, file, email)
• Orchestration: CrewAI with scheduled loops

📘 Summary

Agentic agents are powered by a layered tech stack that integrates language understanding, memory, planning, tooling, and scheduling. While the LLM is at the heart of it all, the supporting infrastructure — memory systems, function interfaces, and control logic — is what makes these agents capable of persistent, autonomous, and intelligent behavior.