🤖 AI Agents

The Evolution of AI: From Chat to Agents

We’re moving beyond simple Q&A chatbots to agentic AI, systems that can:

1. Understand complex, multi-step tasks
2. Plan a sequence of actions to achieve goals
3. Execute those actions using tools and APIs
4. Adapt when things don’t go as expected
5. Learn from feedback and improve over time

This is where the real value of AI lies, automation of complex knowledge work.

My Agent Development Framework

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from langgraph.graph import StateGraph, END
from pydantic import BaseModel
from typing import Literal

class AgentState(BaseModel):
    task: str
    plan: list[str] = []
    current_step: int = 0
    results: dict = {}
    requires_human: bool = False

def create_reliable_agent():
    workflow = StateGraph(AgentState)
    
    # Nodes
    workflow.add_node("planner", plan_task)
    workflow.add_node("validator", validate_plan)
    workflow.add_node("executor", execute_step)
    workflow.add_node("checker", check_result)
    workflow.add_node("human_review", request_human_review)
    workflow.add_node("compiler", compile_results)
    
    # Edges with conditional routing
    workflow.add_edge("planner", "validator")
    workflow.add_conditional_edges(
        "validator",
        lambda s: "executor" if s.plan else "planner"
    )
    workflow.add_conditional_edges(
        "checker",
        lambda s: "human_review" if s.requires_human else "executor"
    )
    workflow.add_edge("executor", "checker")
    workflow.add_edge("human_review", "executor")
    
    return workflow.compile()

Agent Patterns I Implement

Tools and Integrations

I build agents that can use:

• APIs: REST, GraphQL, custom integrations
• Databases: Read, write, query structured data
• File Systems: Parse, generate, manipulate documents
• Web: Browse, search, extract information
• External Services: Email, calendar, CRM, etc.

Frequently Asked Questions

How much does it cost to build an AI agent?

AI agent development costs $120-200 per hour for production-quality systems. Project costs: simple task automation agent $20,000-40,000, multi-tool enterprise agent $50,000-150,000, multi-agent systems $150,000-400,000+. Agents are more complex than chatbots, expect 2-3x the development cost. I’ve built production agents at Anaqua processing legal documents autonomously.

What is an AI agent and how is it different from ChatGPT?

AI agents are autonomous AI systems that can: plan multi-step tasks, use tools (APIs, databases, browsers), make decisions, and work toward goals without constant human input. ChatGPT just responds to messages. Agents can book appointments, research topics, write code, and complete complex workflows. They’re the next evolution of AI.

Are AI agents ready for enterprise production in 2025?

Yes, with proper engineering. Production agents need: guardrails against loops, human-in-the-loop for high-stakes decisions, cost controls, observability, and structured outputs. Most demos fail in production because they lack these. I specialize in enterprise-grade agents, not just prototypes. My agents at Anaqua process sensitive legal documents reliably.

LangGraph vs CrewAI vs AutoGen: which AI agent framework should I use?

Choose LangGraph for: complex stateful workflows, fine-grained control, production reliability. Choose CrewAI for: quick multi-agent prototypes, role-based agent teams. Choose AutoGen for: code generation agents, Microsoft ecosystem. LangGraph is my primary choice for enterprise because of its reliability and observability features.

How long does it take to build a production AI agent?

Development timeline: simple single-tool agent 4-6 weeks, multi-tool agent with memory 8-12 weeks, multi-agent enterprise systems 3-6 months. Most time goes to: reliability engineering (40%), tool integration (30%), testing edge cases (20%), core logic (10%). Agents that “work in demos” often take another 2-3 months to productionize.

Related

Experience:

• AI Backend Lead at Anaqua - Built autonomous AI agents
• Founder at Sparrow Intelligence - Agentic knowledge systems

Case Studies: Agentic AI Knowledge Systems | Enterprise RAG System | Multi-LLM Orchestration

Pillar: AI & Machine Learning Services

Related Technologies: LangChain, MCP, OpenAI, Anthropic Claude, FastAPI