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Agents

Presentation - Automating the Future: Build Powerful AI Agents - Google Slides

An LLM Agent is a software entity capable of reasoning and autonomously executing tasks.

GitHub - viktoriasemaan/multi-agent: Examples of AI Multi-Agent Solutions

Building LLM Agents with Tool Use - YouTube

AI Agents Are Changing AWS Cost Prediction - YouTube

Concepts​

  1. Agent: An autonomous entity that perceives, reasons, and acts in an environment to achieve goals.
  2. Environment: The surrounding context or sandbox in which the agent operates and interacts.
  3. Perception: The process of interpreting sensory or environmental data to build situational awareness.
  4. State: The agent’s current internal condition or representation of the world.
  5. Memory: Storage of recent or historical information for continuity and learning.
  6. Large Language Models: Foundation models powering language understanding and generation.
  7. Reflex Agent: A simple type of agent that makes decisions based on predefined “condition-action” rules.
  8. Knowledge Base: Structured or unstructured data repository used by agents to inform decisions.
  9. CoT (Chain of Thought): A reasoning method where agents articulate intermediate steps for complex tasks.
  10. ReACT: A framework that combines step-by-step reasoning with direct environmental actions.
  11. Tools: APIs or external systems that agents use to augment their capabilities.
  12. Action: Any task or behavior executed by the agent as a result of its reasoning.
  13. Planning: Devising a sequence of actions to reach a specific goal.
  14. Orchestration: Coordinating multiple steps, tools, or agents to fulfill a task pipeline.
  15. Handoffs: The transfer of responsibilities or tasks between different agents.
  16. Multi-Agent System: A framework where multiple agents operate and collaborate in the same environment.
  17. Swarm: Emergent intelligent behavior from many agents following local rules without central control.
  18. Agent Debate: A mechanism where agents argue opposing views to refine or improve outcomes.
  19. Evaluation: Measuring the effectiveness or success of an agent’s actions and outcomes.
  20. Learning Loop: The cycle where agents improve performance by continuously learning from feedback or outcomes.

Building​

How to Build an AI Agent (7-Step Blueprint)

  1. System Prompt - Define the agent’s goals, role, and instructions. A thoughtful prompt shapes behavior from the ground up.
  2. LLM Selection - Pick your reasoning engine (e.g. GPT-4, Claude, Gemini) and tune it with parameters like temperature and max tokens.
  3. Tools - Give your agent abilities: from calling APIs to using other agents as tools. This is where agents move from “talking” to “doing”.
  4. Memory - Short-term and long-term memory (episodic, vector DBs, file stores) allow agents to remember, learn, and personalize over time.
  5. Orchestration - This is the brain behind the brain — workflows, triggers, A2A protocols, and message queues to structure intelligent behavior.
  6. User Interface - A good interface (chat, voice, web) brings your agent to life. It’s not just about function — it’s about trust and usability.
  7. AI Evaluations - Agents need feedback loops. Measure performance, learn from failure, and improve continuously.

Choosing the Right Agentic Framework

Not all platforms are created equal. Some are no-code, some are open-source, some focus on orchestration, and others on ease of integration.

  • OpenAI Agents API – Great for GPT-native agents, thread-based logic
  • Google Vertex AI – Strong orchestration + enterprise-ready
  • LangGraph – DAG-based workflows for complex multi-agent flows
  • AutoGen / CrewAI – Agent-to-agent communication with tool chaining
  • Make / n8n – Ideal for no-code, scenario-based automation

Understanding MCP (Multi-Agent Communication Protocol), memory integration, and orchestration style is essential when scaling from simple prompts to full systems.

SmolAgent - Agents​

Building your agent​

To initialize a minimal agent, you need at least these two arguments:

  • model, a text-generation model to power your agent - because the agent is different from a simple LLM, it is a system that uses a LLM as its engine. You can use any of these options:

  • tools, a list of Tools that the agent can use to solve the task. It can be an empty list. You can also add the default toolbox on top of your tools list by defining the optional argument add_base_tools=True.

CrewAI​

Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.

Why CrewAI?​

The power of AI collaboration has too much to offer. CrewAI is a standalone framework, built from the ground up without dependencies on Langchain or other agent frameworks. It's designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions.

Agno​

Developers use Agno to build Reasoning Agents, Multimodal Agents, Teams of Agents and Agentic Workflows. Agno also provides a beautiful UI to chat with your Agents, pre-built FastAPI routes to serve your Agents and tools to monitor and evaluate their performance.

Use Agno to build the 5 levels of Agentic Systems:

  • Level 1: Agents with tools and instructions.
  • Level 2: Agents with knowledge and storage.
  • Level 3: Agents with memory and reasoning.
  • Level 4: Agent Teams that can reason and collaborate.
  • Level 5: Agentic Workflows with state and determinism.
# pip install -U agno

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.reasoning import ReasoningTools
from agno.tools.yfinance import YFinanceTools

reasoning_agent = Agent(
    model=Claude(id="claude-sonnet-4-20250514"),
    tools=[
        ReasoningTools(add_instructions=True),
        YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True),
    ],
    instructions="Use tables to display data.",
    markdown=True,
)

GitHub - agno-agi/agno: Full-stack framework for building Multi-Agent Systems with memory, knowledge and reasoning.

A beautiful UI for your Agents - Agno

agno/cookbook/getting_started/05_agent_team.py at main · agno-agi/agno · GitHub

Introducing Agno 2.0 and AgentOS: A High-Performance Runtime for Multi-Agent Systems | Ashpreet B. posted on the topic | LinkedIn

AI Agents / Tools​

AI Agents Landscape

Examples​

VertexAI​

References​