GIEMSA AI TECHNICAL LEAD
ERIC GIEMSA : HUMAN-CENTERED AI
PERSONAL PORTFOLIO
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HUMAN-LED AI : CLEAR ROLES, GROUNDED KNOWLEDGE, SHARED LEARNING
CANVAS_COORD: X:0640 Y:0320
VIEW: ALL WORKFLOW LAYERS
FIG. 94-M / HUMAN-LED, AGENT-EXECUTED DELIVERY WORKFLOW INTAKE / DESIGN / BUILD / VALIDATE / RELEASE SYSTEM FLOW / REV. 04 / HUMAN AUTHORITY RETAINED AT DECISION GATES NOT TO SCALE 01 / CONCEPT & DECOMPOSITION 02 / PARALLEL DEVELOPMENT 03 / PR CREATION & VALIDATION FEEDBACK / REWORK TRADE-OFFS SPEC QUESTIONS MERGE CONFLICTS HUMAN / STRATEGIC INPUT Goal definition Scope boundaries & guardrails AGENT-01 / REQUIREMENTS Requirements Agent Stories, acceptance & scope AGENT-02 / ARCHITECTURE System Architect System design & interfaces REPOSITORY / MEMORY Codebase context Patterns & system knowledge ATOMIC TASKS & INTERFACES HUMAN ADVISOR / ON-DEMAND Sparring & guidance Trade-offs, gaps & escalation ORCHESTRATION / DISPATCH Orchestrator Agent Coordinate & integrate work DEV-01 / BACKEND Backend implementation DEV-02 / FRONTEND Frontend implementation DEV-03 / DOMAIN Domain logic PULL REQUEST / CHANGESET Pull request agent Changes & evidence QA-01 / VERIFICATION QA test agent Functional & regression tests QA-02 / ASSURANCE Security & quality agent Risk & code review CONSOLIDATED PR DOSSIER Diff / tests / review notes HUMAN / FINAL AUTHORITY Acceptance & merge approval Decision gate AGENT-06 / RELEASE Docs & release package Staging preparation DELIVERY / COMPLETE Staging deployment Documentation updated
● Workflow ready.

AI SHOULD MAKE GOOD WORK CLEARER — NOT JUST FASTER.

As a technical lead in AI, I focus on the conditions that make human–AI collaboration useful: clear tasks, explicit boundaries, a mandate to act, and feedback that improves both the agent and the process.

Eric Giemsa : AI Technical Lead, Product Owner & Project Lead.

FIELD NOTES : HUMAN + AI

What agent design teaches us about working together

The same clarity that helps an AI act reliably helps people collaborate with less friction.

01 / Make the mandate explicit

A role is more than a prompt.

Define the goal, required inputs, decision boundaries, and what to escalate. Clear responsibilities replace guesswork for both agents and teammates.

02 / Match autonomy to risk

Let safe work move; keep consequential choices human.

Analysis and drafts can be reviewed and revised. Actions with real-world consequences need deliberate human approval.

03 / Treat knowledge as infrastructure

Ground answers in maintained sources.

Retrieval can connect an agent to organizational knowledge, but it cannot make stale or contradictory documentation trustworthy. Ownership and review matter.

04 / Turn errors into feedback

Improve the system, not just the answer.

When results miss the mark, check the role, tools, context, and process. A useful review loop helps people and AI learn from the same failure.

Designing for AI is applied organizational development.

Documenting how work gets done can reduce coordination overhead, make knowledge shareable, and give people clearer room to act. AI is valuable when it strengthens that human system.

⊕ HUMAN GOALS TO STAGING DEPLOYMENT| ⊕ HUMAN ADVISOR ON DEMAND, FINAL HUMAN APPROVAL| ⊕ PARALLEL DEVELOPMENT AGENTS ON ATOMIC TASKS| ⊕ >$100M TECHNICAL PROJECT LEADERSHIP| ⊕ BATTERY-TEST HARDWARE-IN-THE-LOOP SYSTEMS| ⊕ 77-81 GHz RADAR SENSOR FUSION RESEARCH|
WORKFLOW NODE

NODE SPECIFICATION

Operational Function
Select a workflow stage to view its specification.
Ownership / Role
Executor
Trigger
Governance Mode
Input & Output Artifacts
INPUT:
OUTPUT:
Verification & Guardrails
    Architecture Notes