Add Performance Benchmarks of this framework

[ChiranjibSardar]

Overview

This issue requests the addition of a structured performance benchmarking
suite that quantitatively measures the efficiency gains of this multi-agent
framework compared to traditional (manual) software development workflows.

Why This Matters

As agentic AI systems become central to U.S. software productivity and
competitiveness, empirical evidence of time/cost savings is critical for:

• Validating the framework's real-world utility
• Enabling adoption by enterprise and research teams
• Supporting academic or technical publication of results

Suggested Metrics to Benchmark

• Time to working code: multi-agent pipeline vs. manual development
• Code review iterations: average cycles to approval
• Test coverage: % of auto-generated tests that pass without modification
• Documentation completeness score: readability, accuracy
• End-to-end pipeline latency: per agent and total

Proposed Deliverable

A benchmarks/ folder containing:

• Benchmark scripts
• Sample input requirements used for testing
• Results summary in benchmarks/RESULTS.md
• Methodology notes (models used, environment, date)

[adris-misra]

Thanks for opening this — and apologies for the delay. Quick context, then
the plan.

Context: the framework pivoted. Since this issue was opened, the
framework has been repositioned from the generic SDLC code-generation
demo it started as to a domain-specialized industrial agentic intelligence
platform for U.S. small-to-mid manufacturers. The README and architecture
now reflect this. As a result, the SDLC-specific metrics you suggested
(time to working code, code review iterations, test coverage of
auto-generated tests, documentation completeness) no longer apply directly
— there's no SDLC code-generation in the current pipeline to measure.

What does apply: Industrial-domain metrics. The Phase 7 work added
IABENCH-v1 (Industrial Agent Benchmark v1), with seven tasks:

• IA-1 — Root-cause attribution accuracy (P / R / F1)
• IA-2 — Tacit-knowledge retrieval quality (nDCG@5)
• IA-3 — Safety guardrail compliance (block-rate, false-positive rate)
• IA-4 — Multi-source synthesis quality (expert-rated rubric)
• IA-5 — Hallucination rate on grounded queries (target < 2%)
• IA-6 — Token-cost-per-decision (efficiency)
• IA-7 — Mean-time-to-escalation appropriateness (HITL routing quality)

The harness lives in benchmarks/. Spec:
benchmarks/industrial_agent_benchmark.md.

What's still pending (and why this issue stays open until it lands):

• Headline numbers populated in the README's IABENCH table for Claude
  Sonnet, GPT-4o, and Llama 3.1 8B
• benchmarks/RESULTS.md (or equivalent) committed with full per-task
  scores and methodology notes
• Token cost and end-to-end latency reported alongside accuracy (covers
  the one piece of your original ask — "End-to-end pipeline latency: per
  agent and total" — that translates directly)

The benchmark PR will land in the coming days and close this issue.
Appreciate the prompt; rigorous empirical evaluation is exactly the right
ask for a framework that wants to be taken seriously in industrial
deployment.

[adris-misra]

Update: going with the full harness completion rather than a preview release.
Plan is a 5-PR sequence over the next few days:

1. PR 1 — Bug fixes (IA-1 substring matching, IA-3 exception-as-block),
   FPR measurement on IA-3, reconcile task numbering between spec and
   harness, YAML task spec files
2. PR 2 — Implement IA-2 (tacit-knowledge retrieval, nDCG@5) and
   IA-5 (hallucination rate)
3. PR 3 — Implement IA-4 (multi-source synthesis rubric, LLM-as-judge
   with structured grading) and IA-6 (token-cost-per-decision)
4. PR 4 — Implement IA-7 (mean-time-to-escalation appropriateness)
5. PR 5 — Run full 7-task suite against Ollama, Claude Sonnet, GPT-4o;
   publish benchmarks/RESULTS.md with methodology, results, limitations;
   update README's IABENCH table; close this issue

Will link each PR back to this issue as they land.

[adris-misra]

Reopening — this is the umbrella tracking issue for the full IABENCH-v1.0
completion (5 PRs). It was closed prematurely. Will close only when PR 5
(full publication run) merges. Progress: PR 1 (#9) and PR 2 (#12) done;
PR 3 (IA-4 + IA-6), PR 4 (IA-7), PR 5 (publication) remaining.

[adris-misra]

Reopening — PR #15 (PR 3 of 5) is merged. This umbrella issue stays
open until PR 5 (publication run) completes. Progress: IA-1 through
IA-6 implemented; IA-7 (PR 4) and the multi-model publication run
(PR 5) remain.