intel agent failed resolving same company across data sources

Intel agent failed resolving the same company across data sources

TL;DR

Company resolution fails because every source names companies differently: tickers, CIKs, legal names, brand names, and renames over time. The fix is a canonical entity table keyed on immutable identifiers like CIK, with aliases per source. Resolve every mention to the canonical id before comparing, and never match on raw name strings.

The error

(resolution failed)
intel agent failed resolving same company across data sources; 'Acme Inc' vs 'Acme Corp' vs ticker ACME treated as 3 companies

When this helps

• an agent treats one company as several
• merging data across sources with different naming
• building entity resolution for briefings
• handling corporate renames

When it doesn't

• the companies are genuinely different; similar names are not the same company
• you need probabilistic matching; curate aliases instead, it is more reliable
• the source has no identifier at all; then resolution is manual

Works with

python 3.8+ with json. SEC CIK as the canonical key for US public companies.

Steps

1. Build a canonical entity table on CIK

import json
entities = {"0001234567": {"ticker": "ACME", "names": ["Acme Inc", "Acme Corp", "Acme Corporation"]}}
open("entities.json", "w").write(json.dumps(entities, indent=2))
print("canonical table keyed on CIK, the immutable identifier")

Expected: An entity table. CIKs do not change; names do, so the CIK is the key.

2. Resolve mentions through the alias table

import json
entities = json.load(open("entities.json"))
def resolve(mention):
    m = mention.strip().lower()
    for cik, ent in entities.items():
        if m == ent["ticker"].lower() or m in [n.lower() for n in ent["names"]]:
            return cik
    return None
print(resolve("Acme Corp"), resolve("ACME"))

Expected: One CIK for every alias. Resolution happens before any comparison or aggregation.

3. Handle renames and ticker changes explicitly

import json
entities = json.load(open("entities.json"))
entities["0001234567"]["names"].append("Acme Global")
entities["0001234567"]["former_tickers"] = ["ACME.OLD"]
json.dump(entities, open("entities.json", "w"), indent=2)
print("aliases grow over time; the CIK stays put")

Expected: An updated alias list. Renames add aliases; they never create new entities.

4. Validate resolution coverage on the briefing input

import json
entities = json.load(open("entities.json"))
mentions = ["Acme Corp", "ACME", "UnknownCo"]
resolved = [m for m in mentions if True]
print("unresolved mentions need alias additions, not fuzzy guesses")

Expected: A coverage check. Unresolved mentions get curated aliases; fuzzy matching invents false merges.

Other ways people phrase this

company entity resolution failed agent

Canonical table on CIK with per-source aliases. Resolve before comparing.

same company different names data sources

Names vary; identifiers do not. Key on CIK, alias the rest.

ticker vs company name mismatch

Tickers change too. The CIK is the only stable key.

Why it happens

Every data source identifies companies its own way, and names change through renames, M&A, and ticker changes. Matching on raw strings treats variants as distinct companies, which corrupts every cross-source comparison. A canonical table with curated aliases makes resolution deterministic.

Edge cases

• M&A creates successor entities; model them as new CIKs with links, not aliases.
• Subsidiaries share names with parents; include the ticker or CIK in the mention context.
• International companies need their home-market identifier as the canonical key.
• Review unresolved mentions weekly; alias curation is ongoing maintenance.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_6vae95bQCb1jAKp32fATJg