The ElasticPM Framework

AI-Native Project Management for High-Uncertainty R&D

ElasticPM is a specialized project management framework designed for high-uncertainty environments: deep tech R&D, scientific discovery, and AI-native product development.

It addresses the fundamental failure mode of traditional management in these domains: you cannot schedule discovery, but you must operationalize it.

The Core Philosophy: "Operationalizing Chaos"

Traditional methodologies (Waterfall, Standard Agile) assume a known destination or at least a clear path to the next milestone. In cutting-edge research or AI development, the "correct" path is often unknown until it is built.

ElasticPM replaces rigid planning with Elastic Workflows—systems that expand to explore ambiguity and contract to deliver rigor.

1. Macro-Stability, Micro-Elasticity

Instead of choosing between Waterfall's predictability and Agile's flexibility, ElasticPM hybridizes them:

  • Strategic Layer (Waterfall-like): Fixed budgets, rigorous compliance gates, definitive "Kill/Keep" milestones.
  • Execution Layer (Deeply Agile): Rapid, parallelized iterations driven by AI agents. Scope is allowed to expand ("explode") horizontally to capture all possibilities, then is aggressively trimmed via falsification gates.

2. The "Wrapper" Concept: Managing Ambiguity

Most PM frameworks force early convergence: "Pick Option A or Option B to proceed." In high-stakes R&D, early convergence is often fatal.

ElasticPM utilizes a "Wrapper"—a formal container for holding mutually exclusive hypotheses or conflicting constraints simultaneously.

This turns Parallel Execution into a risk mitigation strategy, enabled by the low cost of AI compute.

3. Bayesian Decision Gating

ElasticPM uses Bayesian Confidence Scoring instead of binary "Done" states. We ask: "What is our confidence level (0.0 - 1.0) that this component will integrate with the final system?"

The Engine: AI-Native Orchestration

ElasticPM relies on Agentic Stacking:

  1. Specialized Agents: AI units acting as "Junior Developers" equipped with strict SOPs.
  2. Adversarial Loops: One agent builds, another agent critiques. The human PM acts as the Systems Architect.
  3. Elastic Scale: Spinning up 10 parallel experiments overnight.

Case Study: Serendipitous Discovery

While researching baryogenesis, a number theory anomaly appeared. Instead of letting it derail the physics timeline, I created a "Side Project Wrapper."

I spun up a specialized agent team to verify the pattern while I continued the physics work. Outcome: A new contribution to number theory published with zero delay to the primary roadmap.

The ElasticPM Toolkit

The Hypothesis Map

A living document that replaces the traditional "Project Plan" in the early discovery phase.

ID Hypothesis Confidence Evidence Required Status
H-1 GNNs can predict complexity phase transitions. 0.2 (Low) 90% accuracy on synthetic data In Progress
H-2 TDA is required for phase detection. 0.5 (Med) Betti number convergence Pending

Services & Consulting

I offer Technical Program Management and Methodology Consulting for organizations ready to transition to AI-Native Discovery.

R&D Audit

Assessing team readiness for agentic workflows.

Workflow Design

Custom Elastic Workflows for your domain.

Interim TPM

Leading high-uncertainty initiatives from 0 to 1.

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