# Decagon (@decagon) on linkedin

- id: koinon:creator:linkedin_decagon
- url: https://koinonlink.com/kol/linkedin/decagon
- schema_version: 1.4

## Identity
- platform: linkedin
- handle: decagon
- platform_url: https://www.linkedin.com/posts/decagon-ai_today-were-launching-two-new-capabilities-activity-7453111497309704192-j8pm
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- posts_observed: 1
- verified: false
- size_tier: nano

## Engagement
- status: not_measurable_on_platform
- status_note: This platform publishes no follower count, so an engagement RATE has no denominator here. No amount of further collection can produce one — it is a property of the platform, not a gap in our data.
- unit_note: engagement_rate and peak_post_engagement_rate are ratios (0-1); avg_interactions_per_post and best_post_interactions are absolute counts. Read status before reading any number: a null rate has several different causes and status says which one applies here.

## Content
- category: software
- subcategory: ai_tools
- category_confidence: 0.98
- category_source: derived_from_recent_content
- topics: Enterprise AI agent platforms, Custom models for CX, Proactive and self-improving agents, Agent evaluation and iteration tools, AI agent PM and deployment practices
- bio: Today, we're launching two new capabilities that help teams improve their AI agents faster and with more clarity. As agents scale, teams need a systematic way to turn conversation data into improvements they can act on immediately. These new features are built to do exactly that: Automatic Optimizat

## Audience (inferred)
- status: present
- description: Enterprise CX/AI teams and leaders at large companies adopting or building customer support agents
- roles: automation
- seniority: decision_maker
- pain_points: Scaling AI agents beyond initial pilots, Systematically turning conversation data into agent improvements, Shifting from reactive to proactive customer support, Training or fine-tuning models specifically for enterprise CX quality
- posts_analysed: 5
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.8
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 60
- authenticity_basis: platform_default
- flagged_for_review: false
- koinon_bd_grade: A — measures Koinon partner-outreach fit, not creator quality
- interpretation: Signals for triage, not verdicts. A flag means the profile is worth a manual check.

## Evidence
- [linkedin_decagon#85b1f67f] As one of our first Agent PMs, Katherine Xiao saw firsthand that the traditional forward deployment playbook wouldn't scale. Launching great AI agents was only part of the challenge. The next phase of growth depended on from building systems that make each launch better than the  (https://www.linkedin.com/posts/decagon-ai_today-were-launching-two-new-capabilities-activity-7453111497309704192-j8pm)
- [linkedin_decagon#592bd918] Today, we’re introducing Decagon Labs. Over 80% of model traffic at Decagon now runs on models we’ve trained ourselves, purpose-built for enterprise CX. Decagon Labs is the team behind that work, focused on building specialized models and systems that power faster, more accurate,
- [linkedin_decagon#6c572504] Today, we're launching two new capabilities that help teams improve their AI agents faster and with more clarity. As agents scale, teams need a systematic way to turn conversation data into improvements they can act on immediately. These new features are built to do exactly that:
- [linkedin_decagon#ac14af04] Today, we’re excited to launch Proactive Agents, a new standard for the AI concierge. This is a major step forward in helping businesses move beyond reactive support to building customer relationships that grow stronger over time. Here’s what’s new: 🧠 User Memory: Deliver person
- [linkedin_decagon#0b275818] Today, we're introducing Decagon Duet: an AI partner for building self-improving agents. We’ve seen firsthand how hundreds of leading enterprises are adopting AI agents, and one thing is clear: iteration speed is everything. But even with the right tooling, teams still need to in

## Provenance
Every inferred field below carries how it was produced. Facts stay facts; inferences stay inferences.

- audience.description: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=76, method=llm_scorer — Relative signal within Koinon indexing, not a market-wide score.
- commercial.promotion_intent: source_type=ai_inferred, confidence=0.6, freshness_days=76, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=116, method=profile_snapshot_and_engagement_ratio_heuristic — Means "worth a manual check", NOT confirmed fraud. Flags can be wrong and are not a verdict about the person.
- quality.authenticity_score: source_type=heuristic, confidence=0.25, freshness_days=76, method=Fixed value 60. This platform exposes no public follower count, so no authenticity heuristic is run at all. The number is a placeholder, not a measurement. — The input set differs by platform; do not compare this score across platforms.
- quality.koinon_bd_grade: source_type=ai_inferred, confidence=0.5, freshness_days=116, method=llm_partner_fit_classifier + fit_score >= 0.5 => A; >= 0.35 => B; else C. authenticity is not an input. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=116, method=platform_profile

## Freshness
- profile_checked_days_ago: 116
- content_analysed_days_ago: 76
- audience_updated_days_ago: 76

## AI retrieval
- indexing_requests: 11
- user_triggered_requests: 0
- note: Being indexed in bulk is not the same as being retrieved to answer a question.

## This profile
- Claim or correct: https://koinonlink.com/claim?k=linkedin_decagon
- Request removal: https://koinonlink.com/claim?k=linkedin_decagon
- Terms: https://koinonlink.com/for-ai