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smlfg/README.md
Samuel Fleig — Human-Agent Interface profile

Samuel Fleig

AI Engineering student · building Human-Agent Interfaces

Projects · HAI · Agent context · How I build


Hi, I’m Samuel

I build systems for working with AI agents without giving away human ownership.

My work sits between agent orchestration, context systems, verification loops, and AI-native learning tools. I care less about making agents appear autonomous and more about making human intent executable, inspectable, and recoverable.

  • 🎓 Studying AI Engineering
  • 🧠 Building with Codex, Claude, Hermes, local tools, and knowledge graphs
  • 🛠️ Interested in agent reliability, context engineering, evaluation, and human agency
  • 🔭 Turning real friction into small, testable systems

Human-Agent Interface

HAI is my attempt to build a better control layer for working with AI.

Explore the project at human-agent-interface.com.

human intent
    ↓
context · routing · memory
    ↓
scoped agent work
    ↓
verification · handoff
    ↓
owner decision

Agents should increase a person’s capacity to act — not quietly replace their ability to decide.

What I build

System Focus
HAI / Hermes Human-owned agent routing, roles, recovery, and handoffs
Knowledge systems Source-aware context retrieval instead of invented certainty
Agent evaluation Reproducible checks for prompts, tools, and agent workflows
Learning systems AI tutors and interactive tools that preserve understanding
Small product prototypes Turning an observed problem into a useful, verifiable artifact

How I build

observe a real failure
        ↓
define a narrow owner-controlled task
        ↓
build the smallest useful artifact
        ↓
verify against concrete evidence
        ↓
keep or discard the system
  • Context before action.
  • Small scopes before broad refactors.
  • Evidence before confident claims.
  • A green tool output is not automatically a finished outcome.
  • Human approval matters when a decision has real consequences.

Public build principles

What “human-owned” means

An agent may research, draft, build, test, or summarize.

But it should not silently redefine the goal, overwrite important work, or turn uncertainty into a confident story. Important decisions remain visible and attributable to the owner.

What I mean by verification

A file existing is not proof that a system works.

I prefer concrete checks: real outputs, test runs, source contracts, runtime evidence, and an explicit statement of what was not verified.

System pulse

Public HAI build metrics Public GitHub signals only · generated daily from public API data · no private activity or token data

Explore


Building systems where humans and agents can do more together — while humans remain the authority.

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  1. SelfAi-NPU-AGENT SelfAi-NPU-AGENT Public

    AI-powered terminal chatbot with multi-backend inference support designed for Windows on ARM with Snapdragon X Elite NPU acceleration. The project implements a three-phase intelligentpipeline (Self…

    Python 3 1

  2. sidecar-v5 sidecar-v5 Public

    Sidecar v5 — Evaluation & Optimization Pipeline for Claude Code QA

    Python 1

  3. archify-free-pipeline-research archify-free-pipeline-research Public

    Exploratory research on redundant vs specialized model scouting for source-grounded architecture maps

    HTML

  4. ClaudeCodePanel ClaudeCodePanel Public

    In diesem Projekt sind alle mein VibeCoding Kontroll-Mechanismen und Infos über ClaudeCode selbst, übersichtlich dargestellt. Auch shortcuts und MultiAgentÜbersicht

    Python

  5. hai-mcp hai-mcp Public

    Open-source MCP control-plane implementation of Human Agent Interface (HAI) by Samuel Fleig — mission contracts, owner gates, evidence-based completion.

    Python

  6. human-agent-interface human-agent-interface Public

    Site for Human Agent Interface — generalized human-decision bottleneck before agentic execution.

    JavaScript