Built for the #H0Hackathon — Hack the Zero Stack with Vercel v0 and AWS Databases
Most AI pipelines follow a fixed script: input in, output out, nobody checks the work. For the H0 hackathon (Track 2: Monetizable B2B App), I built ChemSpectra Agent — an FTIR spectral analysis system where the AI verifies its own conclusions and self-corrects when evidence conflicts.
Instead of hardcoding which tools to call, the agent uses a ReAct loop with Qwen-3.7-Max function calling. The LLM autonomously selects from 5 tools — identify_material
(130K+ reference spectra), explain_peaks
, assign_functional_groups
, match_library_topk
, and search_public_results
. A material ID request might trigger two tools; a deformulation request triggers all three analytical tools. The LLM decides, not the developer.
After tools return results, _detect_evidence_conflicts()
compares outputs. If identify_material
says "PET" but assign_functional_groups
found no ester groups, that's a contradiction:
expected_groups = {
"pet": ["ester", "c=o", "aromatic"],
"nylon": ["amide", "n-h", "c=o"],
}
The agent estimates confidence from match scores, candidate score gaps, and functional group coverage. Below 0.75 confidence or any conflicts, a verification round fires automatically:
needs_verification = (
confidence < 0.75 or len(conflicts) > 0
)
The agent gets told exactly what went wrong and calls additional tools to investigate. Post-verification confidence is logged, creating traces like [0.62, 0.84]
.
Every session persists to DynamoDB with 30-day TTL — tool call logs, confidence traces, synthesis, final report. But we went deeper than basic CRUD:
gsi-created
(partition: ALL
, sort: created_at
) replaces full-table scan with efficient time-ordered query; gsi-material
(partition: top_match
, sort: created_at
) enables "show me all PET analyses" aggregationchemspectra-stats
table tracks total_analyses
and total_tools_called
via DynamoDB ADD
operations, safe under concurrent requestsattribute_not_exists(session_id) OR step <> :confirmed
to prevent concurrent overwrites of finalized reportsRegulated industries (pharma, forensics) require this audit trail. DynamoDB fits because the primary access is single-item by session_id
, the GSIs cover the two secondary patterns, and TTL handles cleanup automatically.
The loop runs 2-4 iterations in under 30 seconds. Self-repair for malformed LLM JSON has near-100% recovery. This turns "AI that gives answers" into "AI that checks its work" — essential when reports go into regulatory filings.
Try it: chemspectra-agent-h0.vercel.app | Code: github.com/jxbaoxiaodong/chemspectra-agent-h0
This article was written as part of my participation in the H0 AWS+Vercel Hackathon.