# Agentic AI Foundation (@AgenticAIFdn) on x

- id: koinon:creator:x_agenticaifdn
- url: https://koinonlink.com/kol/x/AgenticAIFdn
- schema_version: 1.4

## Identity
- platform: x
- handle: AgenticAIFdn
- platform_url: https://twitter.com/AgenticAIFdn
- website: http://aaif.io
- country: United States
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 2312
- following: 10
- posts_total: 569
- posts_observed: 2
- posts_per_day: 2.025
- account_age_days: 281
- verified: true
- size_tier: nano

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.00346
- best_post_interactions: 8
- measurement_source: x_post_interactions
- measurement_formula: interactions on one post — likes, or likes + reposts + replies depending on the discovery path (the two are not distinguishable in the stored data)
- comparable_pool: peak_interactions
- status_note: We have only the single best observed post, so no mean exists. peak_post_engagement_rate is an UPPER BOUND, systematically higher than an average — never compare it against an average rate.
- 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: 0.95
- category_source: derived_from_recent_content
- topics: Model Context Protocol (MCP), Agentic AI development, Open source AI projects, AI agent conferences
- bio: AAIF is a neutral, open foundation ensuring AI capabilities evolve transparently and collaboratively, advancing adoption of leading open source AI projects.

## Audience (inferred)
- status: present
- description: AI developers and practitioners building agentic systems with open protocols like MCP
- roles: automation
- seniority: practitioner
- pain_points: Adapting rigid APIs to fast-evolving AI agent workflows, Keeping up with new standards like Model Context Protocol, Finding relevant technical events and community access in agentic AI
- posts_analysed: 3
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.7
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 59
- authenticity_basis: engagement_observed
- credibility_score: 59
- flagged_for_review: false
- koinon_bd_grade: B — 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
- [x_agenticaifdn#3724cba7] Most APIs are built to stay fixed, designed carefully and rarely updated. That makes them a tough fit for fast-moving systems. The real strength of MCP is its flexibility by design. Systems shift every day. MCP gives you a way to describe and share those changes directly, and you (https://twitter.com/AgenticAIFdn)
- [x_agenticaifdn#4c30911a] If you're building with #AI agents, this is a talk you won't want to miss. @dsp_ (co-creator of the #ModelContextProtocol and Member of Technical Staff at @AnthropicAI) is joining the keynote lineup at #AGNTCon + #MCPCon Japan! 🔎 View the full event schedule: https://t.co/fSemte
- [x_agenticaifdn#85c3f336] 🚨 FINAL HOURS 🚨 Early bird pricing for #AGNTCon + #MCPCon Europe ends TONIGHT at 23:59 CEST! Save over €500 on your ticket to join the open #AgenticAI community in Amsterdam (17–18 September)

## 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=80, 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=80, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=112, 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=0.5, freshness_days=80, method=Profile-shape heuristic from a single snapshot (follower count, following ratio, account age, posting volume, verification), reduced for engagement red flags where post engagement was collected. Where only a follower count is available the score saturates at 58. — 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=112, method=llm_partner_fit_classifier + fit_score >= 0.6 AND authenticity >= 60 => A; fit_score >= 0.4 AND authenticity >= 45 => B; else C. authenticity < 30 or a promotional-pattern flag => quarantine. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=112, method=platform_profile

## Freshness
- profile_checked_days_ago: 112
- content_analysed_days_ago: 80
- audience_updated_days_ago: 76

## AI retrieval
- indexing_requests: 9
- 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=x_agenticaifdn
- Request removal: https://koinonlink.com/claim?k=x_agenticaifdn
- Terms: https://koinonlink.com/for-ai