An Example from Development

One goal of the project is to track an user’s skillset. And to do it in a way that can result in a: skill claim — evidence structure. You see this structure in good resumes, in solid self-reviews, or in critical client briefings — “I tuned the database queries to comfortably fit the database into the AWS Reserved Database instances we had, resulting in a 10K a month savings over the old architecture” (True — good folks often pay for themselves in cost avoidance. 😉 ).

One of the tools that I am working on uses a combination of AI Agents and Context Analysis to build the claim-evidence structures for the user. The LLM was wandering around a bit, so I used the prompt techniques: Few-Shot Prompting, and ReAct to tighten the answers a bit. I will explain those in later blogs entries, it is kinda late.

So here is my working result from the agentic workflow. I have some improvement left to do, but we are getting there:

profile:
  title: "Principal Software Engineer / Chief Architect"
  focus: ["Agentic AI systems", "Big-data API platforms", "Secure production architecture"]
  codebases_combined: ["NOAA HEFS API (WRDS)", "DigiMuse.AI"]

core_skills_ranked:
  - skill: "Agentic workflow orchestration (DAG executor)"
    level: "Expert"
    why_it_matters: "Reliable multi-step agent runs (plan→act→observe), retries, deterministic execution"
    proven_in: ["DigiMuse.AI"]
    proof_points:
      - "DAG execution loop with node dispatch and run-state propagation"
      - "Per-node retry/continue-on-error policies"

  - skill: "Tool-using agents (constrained tool registry + validation)"
    level: "Expert"
    why_it_matters: "Safe, debuggable tool invocation with guardrails and bounded execution"
    proven_in: ["DigiMuse.AI"]
    proof_points:
      - "Tool registry with validated execution + timeouts"
      - "Function-dispatch style tools for transforms/actions"

  - skill: "RAG & context assembly with tenant isolation"
    level: "Advanced"
    why_it_matters: "Grounded responses and secure per-tenant data boundaries"
    proven_in: ["DigiMuse.AI"]
    proof_points:
      - "Retrieval node + context assembly node in agent graph"
      - "Document ownership model + user-scoped retrieval queries"

  - skill: "Large-scale data API design (Django/DRF)"
    level: "Expert"
    why_it_matters: "Stable, versioned contracts for high-volume hydrologic data access"
    proven_in: ["NOAA HEFS API (WRDS)"]
    proof_points:
      - "Versioned API surface with OpenAPI operationIds"
      - "DRF routing of core resources (archives/ensembles/headers) + analytics endpoints"

  - skill: "Performance engineering for big result sets (streaming + hard caps)"
    level: "Expert"
    why_it_matters: "Avoids memory blowups; protects database and API under heavy queries"
    proven_in: ["NOAA HEFS API (WRDS)"]
    proof_points:
      - "Streaming JSON responses via generator + queryset iterator"
      - "Explicit limit ceilings and defensive offset/limit handling"

  - skill: "Time-series data modeling & Postgres indexing strategy"
    level: "Advanced"
    why_it_matters: "Efficient query performance at scale for append-heavy temporal tables"
    proven_in: ["NOAA HEFS API (WRDS)", "DigiMuse.AI (storage indexing)"]
    proof_points:
      - "BRIN indexes on datetime fields; targeted/composite indexes for query patterns"
      - "Tenant-scoped document indexing by (user, created_at) and status/type fields"

  - skill: "Auditability, persistence, and observability for complex workflows"
    level: "Advanced"
    why_it_matters: "Reproducible runs, debugging, compliance-friendly trails"
    proven_in: ["DigiMuse.AI", "NOAA HEFS API (WRDS)"]
    proof_points:
      - "Persisted workflow executions (inputs/outputs/state/log/status/timestamps)"
      - "Structured execution log + trace/telemetry events (JSONL) + per-run timing metrics"

  - skill: "Security-first production architecture & ops"
    level: "Advanced"
    why_it_matters: "Reduces blast radius and simplifies compliance posture"
    proven_in: ["DigiMuse.AI"]
    proof_points:
      - "Split-node design: public app node + VPN-only storage node (WireGuard), deny-all posture"
      - "Runbooks: deploy/verify, health checks, troubleshooting, backups"

testing_quality:
  emphasis: "Integration-heavy validation of agent graphs and tool behavior"
  proof_points:
    - "End-to-end tests covering RAG→rerank→context→trace workflows; reported full pass of suite"
  note: "Treat coverage/percent claims as valid only where reports are present."

known_gaps_to_close_for_resume_claims:
  - area: "DigiMuse.AI background execution"
    status: "Not implemented (synchronous DAG runs in HTTP context)"
    upgrade_path: "Add task queue (Huey/Celery/RQ) + idempotency keys + retries at job level"
  - area: "NOAA HEFS API (WRDS) contract/deploy alignment"
    status: "Spec/root advertise endpoints whose routing/behavior may drift from code snippets"
    upgrade_path: "Reconcile OpenAPI + router registration + deployed behavior; add conformance tests"

best_fit_roles:
  - "Principal Engineer (Agentic AI / RAG platforms)"
  - "Staff/Principal Backend Engineer (Big-data APIs, Postgres performance)"
  - "Chief/Lead Architect (secure, production-grade SaaS and platform delivery)"

So this is a pretty solid basis — I do like how the flow called me out for not finishing it. That was interesting.

Did learn we will need a github/gitlab connector for coder users — add it to the RAG code. Another task for the plan.