The patterns, reframed
The 37 DevRel Patterns, Reframed for Agentic Relations
The patterns don't become obsolete. They bifurcate — and understanding which bucket each pattern falls into is the first step in adapting a DevRel program for the two-layer operating environment.
Every activity pattern in Developer Relations Activity Patterns was documented for a human audience. None of the 37 patterns becomes obsolete in the Agentic Relations era. But every one of them bifurcates — some requiring only the addition of an agent-facing variant, some requiring substantive rewriting, and some growing more valuable precisely because they are irreducibly human.
The three categories
Gains an agent layer (15 patterns): the human variant continues largely unchanged. A parallel agent-facing variant is required.
Requires substantive rewriting (8 patterns): the core intent survives, but implementation, participants, metrics, and sometimes the name must change.
Grows more valuable (14 patterns): irreducibly human patterns that increase in strategic value as AI floods every content channel with generic material.
15 patterns
Gains an agent layer
Human variant remains; add an agent-facing one.
8 patterns
Requires substantive rewrite
Intent survives; implementation and metrics change.
14 patterns
Grows more valuable
Irreducibly human; scarcer in an AI-saturated ecosystem.
All 37 patterns at a glance
The full pattern catalog from Developer Relations Activity Patterns, tagged with Agentic Relations status.
Article
Gains agent layerBlog Post
Gains agent layerBook
Gains agent layerGuide
Gains agent layerNewsletter (technical)
Gains agent layerReference Documentation
Gains agent layerSample / Example
Gains agent layerSDK
Gains agent layerSocial Media
Gains agent layerTutorial
Gains agent layerForum
Gains agent layerOpen-Source Project
Gains agent layerSurvey
Gains agent layerTechnical Support
Gains agent layerRecorded Video
Gains agent layerAmbassadors → Agent Champion
Requires rewriteHackathon → Agentic Hackathon
Requires rewriteOffice Hours → Integration Debug Sessions
Requires rewriteTraining → Agent Direction Training
Requires rewriteCode Review
Requires rewriteLive Playground
Requires rewriteCase Study
Requires rewriteWorkshop
Requires rewriteConference
Grows more valuableConference Session
Grows more valuableUser Group Network
Grows more valuableParties
Grows more valuableLive Streaming
Grows more valuableCustomer Check-In
Grows more valuableCustomer Pre-Sale
Grows more valuableSponsorship
Grows more valuableWebinar
Grows more valuablePodcast
Grows more valuablePartnerships
Grows more valuableSwag
Grows more valuableCommunity Newsletter
Grows more valuableBoot Camp
Grows more valuableDeep dive: Reference Documentation reframed
The Reference Documentation pattern exists to give developers the complete technical information they need to use a platform correctly. The implementation assumes a human reader: navigating a hierarchy, reading progressively, resolving ambiguity through context and prior knowledge.
In the Agentic Relations context, Reference Documentation must serve two readers simultaneously. The human reader still navigates, still uses context, still exercises judgment. The agent reader retrieves — it searches for specific sections as context for generation tasks. It cannot navigate ambiguity; it amplifies it into bugs.
The practical implication: every Reference Documentation page should be audited against a standard set of agent queries — "how do I authenticate," "what do I do with a 429," "what is the correct structure for X." If the page doesn't answer those questions directly and unambiguously, agents will generate incorrect integrations using it as context, regardless of how clearly it reads to a human.
Deep dive: Ambassador → Agent Champion
The Ambassador earns trust through authentic community presence. The relationship is the mechanism. Human developers trust Ambassadors because they know them, have seen them speak honestly, have observed them using the platform in real contexts.
AI agents don't operate on trust. They operate on pattern matching against whatever context they have. The "Ambassador" for the agent ecosystem is a practitioner who maintains the corpus of configurations, recipes, and schemas that agents draw on. I call this practitioner the Agent Champion.
The Agent Champion's currency is not reputation. It is a demonstrated success rate: the percentage of AI tool interactions with the platform that produce working integrations because the recipes the Agent Champion maintains are accurate. This is measurable in a way that Ambassador impact never was.
Deep dive: Hackathon → Agentic Hackathon
The traditional Hackathon evaluates what developers build when given creative freedom, tools, and time pressure. The implicit assumption is that build output is primarily a product of developer skill and creativity.
When AI coding agents are the primary builders, that assumption breaks. What gets built in an Agentic Hackathon is primarily a product of how well the team directs its AI tools — and how well the platform's agent-consumable infrastructure supports that direction.
- Direction as a first-class role. Each team has a designated Director who writes the prompts, evaluates agent output, and decides what to accept, reject, or redirect.
- Robustness over completeness. Submissions are stress-tested against error conditions and edge cases, not just demoed.
- Recipe output is required. Teams submit the prompt recipe library they developed during the build.
- Architectural decision logs. Teams document which AI decisions they accepted or overrode, and why.
- Platform monitors, not just mentors. Agent Champions observe team-AI interactions throughout, cataloging where the platform's agent-consumable infrastructure is failing.