AGENTIC AUDIT

Agentic AI & Backend Bullet Auditor

Student Presets:

Project Diagnostic Bench

Raw Score: 42/100
42 /100

High Toy Wrapper Risk

Identified Gaps 4 Critical

Detected Gaps in Current Draft

Missing quantitative evaluation benchmark (accuracy, hallucination rate, or retrieval precision)
No production failure modes (circuit breakers, rate limiting, token budget fallbacks)
Unquantified backend throughput and latency (p95/p99 latency, caching layer)
Abstract algorithm claim without operational integration

Production-Engineered Bullets

Internship Ready
Routing & Fault Tolerance Reliability

Architected multi-agent customer query router in FastAPI and LangGraph, implementing schema-validated function calling and dynamic fallback to smaller local SLMs during API outages.

Retrieval & Latency p95 Optimization

Engineered two-tier RAG retrieval using pgvector with hybrid BM25 search, reducing context window tokens by 38% and dropping p95 retrieval latency from 1.4s to 310ms.

Automated Rigor & Evals Evaluation Suite

Built automated evaluation harness over 450 synthetic edge cases measuring retrieval recall and tool hallucination rate, achieving 94.2% task completion fidelity.

Scale & Token Economics Cost Reduction

Integrated Redis semantic response cache and token rate limiter, cutting third-party model inference costs by 47% under simulated 120 req/sec load.

### Multi-Agent Customer Support Assistant - Architected multi-agent customer query router in FastAPI and LangGraph, implementing schema-validated function calling and dynamic fallback to smaller local SLMs during API outages. - Engineered two-tier RAG retrieval using pgvector with hybrid BM25 search, reducing context window tokens by 38% and dropping p95 retrieval latency from 1.4s to 310ms. - Built automated evaluation harness over 450 synthetic edge cases measuring retrieval recall and tool hallucination rate, achieving 94.2% task completion fidelity. - Integrated Redis semantic response cache and token rate limiter, cutting third-party model inference costs by 47% under simulated 120 req/sec load.

Audit Rubric Matrix: Toy Wrapper vs Production Signal

Evaluation Pillar Typical 5th Sem Student Bullet (High Rejection) Optimized Systems Engineering Alternative Why Recruiters Reject The Former
Evals & Hallucinations "Used LangChain and OpenAI to answer queries with 95% accuracy." "Constructed 450-scenario eval harness with RAGAS, tracking context precision and reducing hallucination to <2.1%." No one achieves "95% accuracy" on open-ended LLMs without an automated ground-truth eval dataset.
Latency & Throughput "Used ChromaDB and embeddings to do fast semantic search." "Configured HNSW indexing in pgvector with Redis semantic cache, dropping p95 latency from 1.8s to 240ms." "Fast" is meaningless. Teams want percentiles (p50/p95), cache hit rates, and embedding dimension costs.
Failure Modes & Resilience "Connected agent tools with OpenAI function calling." "Engineered Pydantic schema validation with exponential backoff and SLM fallback on rate limit / tool error." External LLM APIs routinely fail, throttle, and hallucinate parameters; code without fallbacks won't survive production.

A keyword configuration score does not validate resume achievements

Read the explanation

The default preset has five selected stack labels, four focus signals and four fixed gaps. The score starts at thirty five, adds six per stack and five per focus, then subtracts eight per gap. Stack adds thirty, focus twenty and gaps subtract thirty two. At eight pixels per point the magnitudes measure two hundred forty, one hundred sixty and two hundred fifty six. The final score is fifty three, even though a separate unused preset field says forty two. This is a configuration formula, not an ATS prediction, hiring outcome or proof that the listed technologies were implemented. Removing one active stack label subtracts six score points, changing fifty three to forty seven. Removing one focus instead subtracts five, changing fifty three to forty eight. At five pixels per score original, stack removed and focus removed measure two hundred sixty five, two hundred thirty five and two hundred forty. Gaps remain the selected preset list, not an analysis of the edited raw bullets. The score is clamped between fifteen and ninety five. Toggling labels can improve this displayed score without producing repository evidence or changing the claims in the output. One prewritten optimized bullet claims retrieval latency drops from one point four seconds to three hundred ten milliseconds. The arithmetic difference is one thousand ninety milliseconds, or about seventy seven point nine percent, but the page supplies no measurement evidence for either value. At one fifth pixel per millisecond before, after and claimed reduction measure two hundred eighty, sixty two and two hundred eighteen. The same preset also claims forty seven percent cost savings and ninety four point two percent completion without executing benchmarks. Native exports faithfully package these fixture claims; they must be replaced with verified personal results before use as achievements. No model reviews the edited text or proves the claims true.

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