$ cat services.json

Autonomous Agent Development

Build AI agents that can reason through complex tasks, use tools, and deliver results.

Deliverables:

Task planning and decomposition
Tool calling and function execution
Memory and context management
Structured output enforcement
Error recovery and fallbacks

Multi-Agent Systems

Design systems where multiple specialized agents collaborate on complex workflows.

Deliverables:

Agent role design
Inter-agent communication
Orchestration logic
Conflict resolution
Supervision and monitoring

Human-in-the-Loop Workflows

Build agent systems with appropriate human oversight for high-stakes operations.

Deliverables:

Approval workflows
Confidence thresholds
Escalation paths
Audit trails
Feedback integration

$ man ai-agents

The Reliability Problem in AI Agents

The #1 challenge with AI agents is reliability. Demos are easy; production is hard.

Agents fail because:

LLMs hallucinate tool calls
Plans go off-track mid-execution
Error handling is an afterthought
No observability into agent reasoning

My approach focuses on structured, observable, recoverable agent systems:

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class ReliableAgent:
    def execute(self, task: Task) -> Result:
        plan = self.plan_with_validation(task)
        for step in plan.steps:
            result = self.execute_step_with_retry(step)
            if not result.is_valid():
                return self.escalate_to_human(step, result)
        return self.compile_results(plan)

My Agent Architecture Stack

Orchestration: LangGraph for stateful, resumable agents Validation: Pydantic + Instructor for structured outputs Observability: LangSmith/Langfuse for tracing Persistence: PostgreSQL for agent state and memory Queueing: Celery/Redis for async agent execution

$ cat README.md

The Evolution of AI: From Chat to Agents

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

Understand complex, multi-step tasks
Plan a sequence of actions to achieve goals
Execute those actions using tools and APIs
Adapt when things don’t go as expected
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

Pattern	Use Case	Example
ReAct	Reasoning + Acting	Research agent that searches, reads, summarizes
Plan-Execute	Complex multi-step tasks	Document processing pipeline
Supervisor	Multi-agent coordination	Team of specialized analysts
Human-in-the-Loop	High-stakes decisions	Financial approvals, legal review
Reflexion	Self-improvement	Agent that learns from mistakes

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.

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

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

$ ls -la projects/

Document Analysis Agents

@ Anaqua (RightHub)

Challenge:

Analyze multi-page patent documents, extract entities, and identify conflicts—tasks that took lawyers hours.

Solution:

Built LangGraph-based agents with specialized roles: Extractor, Classifier, Comparator, Reporter. Each agent has structured outputs validated against Pydantic schemas.

Result:

Reduced document analysis from hours to minutes with lawyer-grade accuracy.

Knowledge Management Agent

@ Sparrow Intelligence

Challenge:

Build an agent that can navigate large codebases, understand context, and answer developer questions.

Solution:

Multi-agent system with Repository Navigator, Code Analyzer, and Documentation Generator agents working together.

Result:

Developers get instant, accurate answers about unfamiliar codebases.

Automated Research Agent

@ Sparrow Intelligence

Challenge:

Automate competitive research that required analysts to check multiple sources manually.

Solution:

Web-browsing agent with tool use for search, data extraction, and report generation.

Result:

Generated comprehensive research reports in minutes instead of days.

🤖 AI Agents

$ cat services.json

Autonomous Agent Development

Multi-Agent Systems

Human-in-the-Loop Workflows

$ man ai-agents

The Reliability Problem in AI Agents

My Agent Architecture Stack

$ cat README.md

The Evolution of AI: From Chat to Agents

My Agent Development Framework

Agent Patterns I Implement

Tools and Integrations

$ ls -la projects/

Document Analysis Agents

Knowledge Management Agent

Automated Research Agent

$ diff me competitors/

Build Your AI Agent System