Agentic Systems & Applied AI

Building intelligent, explainable, and inclusive systems that think, act, and adapt.

Agentic systems represent the next leap in AI—moving beyond static models to dynamic, self-directed architectures that reason, orchestrate, and collaborate. I help forward-thinking organizations design and deploy these systems to unlock breakthrough capabilities across user interaction, decision automation, talent systems, and platform intelligence.

I don’t just theorize this future—I build it.

What Are Agentic Systems?

Agentic systems combine LLMs, structured reasoning, and orchestrated autonomy to perform complex tasks proactively. They operate via goal-setting, memory, reflection, and collaboration—enabling AI to move from tool to strategic partner.

Key components include:

  • Multi-agent orchestration: autonomous agents coordinating via planning, negotiation, and feedback.

  • Long-term memory: contextual tracking across sessions, personas, and user goals.

  • Goal-driven behavior: structured reasoning chains, state evaluation, and adaptive decision-making.

  • Knowledge graph integration: dynamic retrieval across structured and unstructured sources.

These are not just prototypes—they are live systems solving real problems.

Some Applications & Systems I’ve Built

TryTheJob: Multi-Agent Job Simulation Platform

An AI-powered job simulation system for career readiness and skills discovery.

  • Multi-phase planner coordinates dynamic challenges tailored to user goals.

  • Persona-based agents simulate job roles (product manager, data analyst, etc.) in dialogue.

  • Goal tracking, feedback, and summaries help users understand progress and gaps.

  • Integrates: Llama 3, FastAPI, Ollama, LangChain-style agent stacks, and persistent user profiles.

Used by: talent platforms exploring experiential hiring models and skill gap diagnostics.

Knowledge Graph–Driven Personal Career Agents

System to unify fragmented profile, skill, job, and learning data into intelligent agents.

  • Custom knowledge graph schema and hybrid search (structured + vector).

  • Semantic query understanding and context-sensitive guidance.

  • Embeds neurodivergent-aware goal models and soft skill tracking.

Applications: inclusive coaching, internal talent mobility, adaptive skill recommendations.

AI Assistant for Innovation Strategy & CTO Offices

A confidential advisory agent built for CTOs and strategy teams to:

  • Surface key research trends from technical and scientific corpora.

  • Draft innovation roadmaps based on tech maturity and risk appetite.

  • Score and rank opportunities using foresight, impact, feasibility, and alignment metrics.

Features: memory, agent critic/reflector models, explainability dashboards.

Ethical AI Governance Agent (Prototype)

A guided agent that:

  • Audits AI pipelines for bias, compliance, and explainability.

  • Maps outputs against the Multi-Dimensional Ethics Framework (MDEF).

  • Flags neurodivergent exclusion risks, traceability gaps, and governance violations.

Used in workshops and advisory contexts to train boards and ethics teams on applied fairness-by-design.

How I Build Them

  • Backend Stack: Python · FastAPI · LangChain · Ollama · Vector DBs (FAISS, Pinecone) · Graph DBs (Neo4j, RDF)

  • Frontend: Next.js · Tailwind · Gradio · UX for explainability and control

  • Agents: Planner · Critic · Summarizer · Persona manager · Goal tracker · Feedback engine

  • Memory: Per-agent state tracking · Challenge lifecycle · Interaction graphs

  • Ethics: Agent introspection, fairness audits, memory boundaries, neurodivergent safeguards

Use Cases I Support

  • Career platforms seeking experiential, agentic job simulation and development.

  • Strategy teams needing internal AI co-pilots for R&D foresight or decision frameworks.

  • Product teams embedding adaptive AI into their applications without hallucination or risk.

  • Organizations needing modular, explainable AI that plays well with humans and systems.

Why It Matters

Most AI systems today are static, opaque, and short-lived in memory. Agentic systems introduce interactional continuity, purpose, and accountability—and they demand ethical scaffolding from day one. My work embeds inclusivity, traceability, and system resilience directly into their design.

I help you build agents that don’t just answer—but understand, adapt, and guide.

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AI Strategy