Incorporating hardware-level energy-metering registers into a blockchain-backed agentic framework enables verifiable, resource-optimized scheduling of security-auditing agents across the software development lifecycle.

This discovery has a Claude-generated validation package with a full experimental design.

Precise Hypothesis

Spine & Adversarial ReadReady for validation

“This hypothesis tests whether integrating hardware energy-metering registers into blockchain-attested scheduling of security-auditing agents can simultaneously reduce measured energy consumption by a verifiable, tamper-evident margin without degrading vulnerability-detection quality.”

• highBlockchain adds consensus and write-latency overhead that likely exceeds any energy savings achieved, making the net system less efficient once attestation overhead is counted — the EVP's own 10-15% latency/energy budgets are suspiciously tight and may not survive real multi-node consensus costs.
  Protocol includes measuring blockchain overhead directly and an abort checkpoint at day 45, but does not yet include a control arm using a non-blockchain tamper-evident log (e.g., simple Merkle-tree signed log) to isolate whether blockchain specifically (vs. any cryptographic attestation) is necessary — this is a methodology gap that should be added before full-scale validation.
• mediumWhy RAPL/security-scanner combination specifically, rather than validating on GPU-bound ML-based auditing agents (which are increasingly common in SAST/SCA tooling) or on ARM/cloud-function environments where RAPL isn't available — the methodology choice privileges a narrow, somewhat dated hardware/tooling stack.
  Boundary conditions explicitly scope this to CPU-bound RAPL-accessible environments and acknowledge GPU/NVML extension as secondary/future work; this is a deliberate scoping choice for tractability but should be stated as a limitation, not resolved within this EVP.
• mediumA 15% energy-savings threshold and 95%-recall-retention threshold appear to be arbitrary round numbers rather than derived from an a priori power analysis or industry benchmark, risking p-hacking or post-hoc justification of thresholds.
  Not yet resolved — a formal power analysis (minimum detectable effect size given n=500 runs and expected variance in joule measurements) should be run before the full-scale study to justify or revise these thresholds; current numbers are reasonable priors from green-computing literature but not derived specifically for this experimental design.

Experimental Protocol

ROI Projection

Implementation Sketch

for each pipeline_run in benchmark_suite:
    agents = [SemgrepAgent, TrivyAgent, ZAPAgent]
    for agent in agents:
        energy_sidecar.start_sampling(agent.pid, interval_ms=100)
    scheduler = EnergyAwareScheduler(agents, constraint="deadline<=T", objective="min_joules")
    plan = scheduler.optimize()  # ILP or greedy bin-packing on (joules, time) tuples
    for task in plan:
        result = task.agent.run(task.target_repo_module)
        joules = energy_sidecar.stop_and_read(task.agent.pid)
        attestation = sign(hash(result.metadata, joules, timestamp), node_private_key)
        blockchain.submit_transaction("recordAudit", attestation)
    consensus.validate_and_commit()
# Adversarial test
inject_forged_attestation(random_joules_value)
assert blockchain.rejects(forged_attestation) == True