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Emerging Tech Explorations

Evaluating emerging standards, tools, and frameworks through rigorous technical spikes to assess their readiness for enterprise adoption.

As an independent consultant, I conduct hands-on research spikes evaluating the practical viability, developer ergonomics, and operational trade-offs of emerging technologies. These evaluations help organizations adopt advanced paradigms with confidence.

Active & Completed Spike Reports

Local-First Analytical Lakehouses

  • Objective: Assess the viability of zero-infrastructure, low-cost analytical prototyping using DuckDB, Polars, and Apache Iceberg.
  • Findings: Prototyping data transformations locally with DuckDB and Polars reduces compute costs during development by up to 90% compared to cloud-based SQL engines, while Parquet/Iceberg metadata allows a seamless export to enterprise cloud lakehouses (BigQuery/ Snowflake) with zero code changes.
  • Recommendation: Adopt immediately for development environments, local ingestion pipelines, and lightweight analytical platforms.

Deterministic Multi-Agent Choreography

  • Objective: Evaluate the trade-offs of cyclic, stateful multi-agent systems built with LangGraph and Pydantic AI against linear chains.
  • Findings: Cyclic multi-agent graphs dramatically improve success rates on complex, non-linear reasoning tasks (like automated diagnostics) by allowing self-correction. However, they require strict schemas (via Pydantic AI) and tracing to prevent runaway LLM loops.
  • Recommendation: Adopt with guardrails. Implement comprehensive runtime observability (Langfuse/OpenTelemetry) and token billing limits.

Policy-as-Code Guardrails for IaC

  • Objective: Test OPA Rego and Conftest to automatically enforce cloud security policies within ephemeral CI/CD pipelines.
  • Findings: Running Conftest validation on Terraform plans before execution prevents security misconfigurations (like public S3 buckets or open GKE clusters) before they are provisioned, reducing auditing overhead and shifting security far left.
  • Recommendation: Mandatory adoption for all multi-account landing zones and enterprise infrastructures.

Self-Healing Observability Loops

  • Objective: Build a prototype using lightweight, local LLMs (like Hermes-3) to parse pipeline logs, diagnose failures, and auto-propose PRs.
  • Findings: Local 8B/70B models excel at identifying standard formatting, query, and timeout issues. Running these loops on-premises preserves data privacy and limits public API exposure while automating repetitive debugging tasks.
  • Recommendation: Evaluating. Excellent for developer assistance and dev-only environments, but human-in-the-loop review is mandatory before merging auto-PRs.