AI & ML interests
You’re building a mesh—so let’s give you artifacts you can actually drop into it. 1. Pipeline contract to embed in your SDK This is the canonical contract your SDK enforces for any model that wants to participate in an Arize AX pipeline. PipelineContract (conceptual) Identity modelId: string spaceId: string organizationId: string projectId: string Schema inputSchema: JSON schema or typed struct outputSchema: JSON schema or typed struct spanSchema: definition of spans (steps, tools, agents) metadataSchema: key/value metadata contract Bindings primaryDatasetId: string evaluationDatasetId: string null monitorIds: string evaluatorIds: string promptGroupId: string null Routing tags: string modelVersion: string promptVersion: string null Constraints invariants: spaceId must match the Space that owns primaryDatasetId projectId is immutable once tracing is set up modelId must be unique within spaceId You can encode this as a TypeScript interface or Python dataclass and make every agent register against it before emitting traces. 2. Routing schema for your agent mesh Think of this as how agents decide where traces go and which pipeline activates. Routing dimensions By space spaceId → pipeline boundary By project projectId → tracing lineage By model modelId, modelVersion → specific model instance By prompt promptId, promptVersion → prompt lineage By tags tags → feature flags, environment, tenant, workflow Example routing schema tags env:prod env:staging env:dev tenant:<tenant-id> workflow:<workflow-name> channel:web channel:mobile channel:kiosk agent:<agent-name> feature:<feature-flag> Routing rule mental model: Monitors, evaluators, and dashboards subscribe to combinations of spaceId, projectId, and tags. Your agents must emit traces with the right combination to land in the intended workflow. 3. Multi-space deployment pattern for retail automation You want a mesh that maps cleanly to business reality. Recommended structure Account Organization: RetailMesh Space: Core-Orchestration Models: router, planner, orchestrator Datasets: global routing logs Monitors: latency, failure rate, routing quality Space: Store-Front Models: customer-facing chat, kiosk assistant Datasets: customer interactions Monitors: CSAT, conversion, safety Space: Back-Office Models: inventory agent, pricing agent Datasets: inventory events, pricing decisions Monitors: stockout risk, margin impact Space: Experiments Models: experimental variants Datasets: A/B logs Monitors: experiment metrics Pattern: Use Spaces to separate: Core orchestration Customer-facing flows Operational flows Experiments/sandboxes Use tags to separate: Tenants Stores Regions Features 4. Model registration template for other developers This is what you hand to teammates as the “fill this out before you ship a model” doc. Model Registration Template Identity Model name: Model ID: Space: Organization: Project ID: Purpose Description: Primary use case: Owner (team/person): Schema Input schema (fields + types): Output schema (fields + types): Span structure (steps/tools): Metadata fields (keys + meaning): Bindings Primary dataset ID: Evaluation dataset ID (optional): Monitor IDs (drift, safety, quality, etc.): Evaluator IDs: Prompt group ID (if applicable): Routing Default tags: Model versioning strategy: Prompt versioning strategy (if applicable): RBAC Required role to manage this model (Space Admin/Member): Who can modify bindings: You can keep this as a YAML/JSON file in the repo and validate it against the pipeline contract. 5. Trace emission scaffold (TypeScript + Python) TypeScript scaffold ts type TraceTag = string; interface TracePayload { model_id: string; space_id: string; organization_id: string; project_id: string; model_version?: string; prompt_id?: string; prompt_version?: string; evaluation_id?: string; input: unknown; output: unknown; spans: unknown; metadata: Record<string, unknown>; tags: TraceTag; } function emitTrace(payload: TracePayload) { // Invariants if (!payload.model_id !payload.space_id !payload.project_id) { throw new Error("Missing required identity fields (model_id, space_id, project_id)."); } // TODO: add your HTTP client / SDK call here // e.g. POST to Arize AX tracing endpoint } Python scaffold python from dataclasses import dataclass from typing import Any, Dict, List, Optional @dataclass class TracePayload: model_id: str space_id: str organization_id: str project_id: str model_version: Optionalstr = None prompt_id: Optionalstr = None prompt_version: Optionalstr = None evaluation_id: Optionalstr = None input: Any = None output: Any = None spans: ListAny = None metadata: Dictstr, Any = None tags: Liststr = None def emit_trace(payload: TracePayload) -> None: if not payload.model_id or not payload.space_id or not payload.project_id: raise ValueError("Missing required identity fields (model_id, space_id, project_id).") TODO: add your HTTP client / SDK call here Example: requests.post(ARIZE_TRACE_URL, json=payload.__dict__) If you want, next step we can: Turn this into a repo-ready spec (README + contracts + examples), or Map it directly onto your current Arize AX space/project setup so it’s not just abstract but live. Today You said ak-e1c64e84-479c-47ca-b893-739cfcf14550-0KlwpHAa-4.txt Plain Text Copilot said Here’s a clean, structured summary of the document you uploaded — focused on the core concepts, responsibilities, and configuration flows for SSO, RBAC, invites, and JIT provisioning in Arize AX. Summary of the Document Single Sign-On (SSO) Arize AX supports SAML2-based SSO. Identity Providers must be configured with: SSO URL / ACS US: https://app.arize.com/auth/v2/saml EU: https://app.eu-west-1a.arize.com/auth/v2/saml EntityID US: https://app.arize.com EU: https://app.eu-west-1a.arize.com NameID format: urn:oasis:names:tc:SAML:1.1:nameid-format:emailAddress Metadata XML is available at /metadata endpoints. SAML configuration includes role mappings, provisioning, and attribute-based permission assignment. Role-Based Access Control (RBAC) Arize AX uses a hierarchical RBAC model across Account → Organization → Space. Account Roles Admin: Full access, including user management, org creation, custom roles, AI provider integrations, and API key creation. Member: Access depends on org/space roles. Developer flag (is_developer): Controls ability to create API keys. Organization Roles Admin: Full org-level control, including member management and integrations. Member: Can create spaces and integration keys; limited editing. Read-only Member: View-only access unless added to private spaces. Space Roles Admin: Full control over projects, dashboards, monitors, datasets, prompts, evaluators, metrics, tags, and data fabric. Member: Write access to model-related entities but cannot manage membership. Read-only: View-only but can use prompt playground. Annotator: Only access to assigned labeling queue items. Inviting Users Users can be invited at the Account, Organization, or Space level. Common invite errors include: Email already exists in Arize Missing required role selection SAML misconfiguration for domains tied to an IdP SSO being enabled (but not enforced) does not affect invites. Configuration Changes Organizations and spaces can be renamed via their respective Settings → Config pages. Project names cannot be changed after tracing setup. Just-In-Time (JIT) User Provisioning Arize supports automatic user creation during SSO login. Key points: Provide a Name attribute for proper first/last name creation. Role assignment can be automated via SAML attribute mappings. Example: mapping "Department": "Ads ML Engineering" to a specific role. Custom roles can also be assigned via SAML attributes. If you want, I can also produce: A shorter TL;DR A diagram-style breakdown A developer-focused cheat sheet A step-by-step setup guide for SSO or RBAC Just tell me what format helps you most. You said how aboutr a developer focused config for correct pipeline workflow assignment as a description of a custom model Copilot said Here’s the developer‑focused configuration you’re asking for: a clear, modular description of how to assign the correct pipeline workflow to a custom model in Arize AX, written for someone architecting agent-aware SDKs, tracing pipelines, and multi‑space RBAC setups. This is not a UI walkthrough — it’s the developer mental model and the RBAC‑aligned configuration flow you need to ensure your custom model lands in the right workflow, with the right permissions, and the right trace routing. ⚙️ Developer-Focused Configuration Correct Pipeline Workflow Assignment for a Custom Model in Arize AX 1. Anchor the Model in the Right RBAC Context Pipeline correctness starts with where the model lives. A custom model must be created inside a Space, and that Space must be inside an Organization whose RBAC roles allow: Space Admin → full pipeline control Space Member → can update model settings, create monitors, send traces Org Admin → can manage AI provider integrations Org Member → can create spaces and integration keys If the model is created in the wrong space or org, your pipeline will mis-route traces or fail due to missing permissions. Developer rule: Always create the model in the Space that owns the pipeline’s datasets, monitors, and dashboards. 🧩 2. Define the Model’s Pipeline Workflow Structure Arize AX pipelines are composed of: Datasets (input, output, evaluation, synthetic, etc.) Monitors (drift, hallucination, toxicity, latency, etc.) Evaluators (LLM-as-a-judge, custom scoring) Prompts (prompt hub) Experiments (model comparisons) Tags (metadata routing) Data Fabric (cross-model lineage) Your custom model must declare: Model Settings → Pipeline Bindings Primary dataset (the canonical input/output pair) Evaluation dataset (optional) Monitor set (drift, quality, safety) Evaluator set (LLM-as-judge or custom) Prompt group (if the model is part of a prompt hub workflow) Tags (for routing traces to the correct monitors) Developer rule: A model without dataset bindings cannot participate in any pipeline workflow. 🔐 3. Assign the Correct Workflow via RBAC-Compatible Model Settings Model Settings → Workflow Assignment This is where you bind the model to the pipeline: Select the Space The model inherits the Space’s monitors, dashboards, and datasets. Bind the Model to a Dataset Input schema Output schema Span structure Metadata fields Enable Pipeline Features Drift detection Quality evaluation Safety evaluation Prompt evaluation Experiment tracking Attach Evaluators Built-in Custom External (Hugging Face, Perplexity, etc.) Attach Monitors Drift Toxicity Hallucination Latency Custom metrics Configure Trace Routing Tags Span types Model versioning Prompt versioning Developer rule: Workflow assignment is just a structured binding of datasets, monitors, evaluators, and routing rules. 🔄 4. Ensure Correct Trace Routing (Critical for Custom Models) Your SDK must send traces with: Required Fields model_id space_id organization_id project_id (immutable once tracing is set up) tags input output spans metadata Optional but Recommended prompt_id prompt_version model_version evaluation_id Developer rule: If the trace lacks the correct space_id or project_id, it will not enter the correct pipeline workflow. 🧱 5. Custom Model Registration Pattern (Developer Mental Model) Think of Arize AX as a mesh of spaces, each with its own pipeline. Your custom model must be registered like this: Code Copy Organization └── Space ├── Model ├── Datasets ├── Monitors ├── Evaluators ├── Prompts └── Experiments If the model is placed in the wrong space, it inherits the wrong pipeline. Developer rule: Spaces are the pipeline boundary. Models inherit everything inside the space. 🛠️ 6. Developer Checklist for Correct Workflow Assignment Before creating the model [ ] Confirm you are Space Admin or Space Member [ ] Confirm the Space has the correct datasets [ ] Confirm the Space has the correct monitors [ ] Confirm the Space has the correct evaluators [ ] Confirm the Space has the correct prompt groups When creating the model [ ] Bind the model to the correct dataset [ ] Enable the correct pipeline features [ ] Attach evaluators [ ] Attach monitors [ ] Configure trace routing tags When sending traces [ ] Include space_id [ ] Include project_id [ ] Include model_id [ ] Include spans [ ] Include metadata [ ] Include tags for routing 🧭 7. Developer-Friendly Description (One-Liner) A custom model’s pipeline workflow in Arize AX is determined entirely by the Space it lives in, the datasets it binds to, the monitors and evaluators attached to it, and the routing metadata included in its traces. If you want, I can also generate: A code scaffold for your SDK’s trace routing A diagram showing pipeline inheritance A RBAC-safe multi-org deployment pattern A custom role definition for pipeline engineers A full workflow template for your retail automation mesh Just tell me what direction you want to take this. You said Here’s the developer‑focused configuration you’re asking for: a **clear, modular description of how to assign the correct pipeline workflow to a custom model in Arize AX**, written for someone architecting agent-aware SDKs, tracing pipelines, and multi‑space RBAC setups. This is **not** a UI walkthrough — it’s the *developer mental model* and the *RBAC‑aligned configuration flow* you need to ensure your custom model lands in the right workflow, with the right permissions, and the right trace routing. --- # ⚙️ Developer-Focused Configuration ## **Correct Pipeline Workflow Assignment for a Custom Model in Arize AX** ### **1. Anchor the Model in the Right RBAC Context** Pipeline correctness starts with *where the model lives*. A custom model must be created inside a **Space**, and that Space must be inside an **Organization** whose RBAC roles allow: - **Space Admin** → full pipeline control - **Space Member** → can update model settings, create monitors, send traces - **Org Admin** → can manage AI provider integrations - **Org Member** → can create spaces and integration keys If the model is created in the wrong space or org, your pipeline will mis-route traces or fail due to missing permissions. **Developer rule:** > *Always create the model in the Space that owns the pipeline’s datasets, monitors, and dashboards.* --- # 🧩 2. Define the Model’s Pipeline Workflow Structure Arize AX pipelines are composed of: - **Datasets** (input, output, evaluation, synthetic, etc.) - **Monitors** (drift, hallucination, toxicity, latency, etc.) - **Evaluators** (LLM-as-a-judge, custom scoring) - **Prompts** (prompt hub) - **Experiments** (model comparisons) - **Tags** (metadata routing) - **Data Fabric** (cross-model lineage) Your custom model must declare: ### **Model Settings → Pipeline Bindings** - **Primary dataset** (the canonical input/output pair) - **Evaluation dataset** (optional) - **Monitor set** (drift, quality, safety) - **Evaluator set** (LLM-as-judge or custom) - **Prompt group** (if the model is part of a prompt hub workflow) - **Tags** (for routing traces to the correct monitors) **Developer rule:** > *A model without dataset bindings cannot participate in any pipeline workflow.* --- # 🔐 3. Assign the Correct Workflow via RBAC-Compatible Model Settings ### **Model Settings → Workflow Assignment** This is where you bind the model to the pipeline: 1. **Select the Space** The model inherits the Space’s monitors, dashboards, and datasets. 2. **Bind the Model to a Dataset** - Input schema - Output schema - Span structure - Metadata fields 3. **Enable Pipeline Features** - Drift detection - Quality evaluation - Safety evaluation - Prompt evaluation - Experiment tracking 4. **Attach Evaluators** - Built-in - Custom - External (Hugging Face, Perplexity, etc.) 5. **Attach Monitors** - Drift - Toxicity - Hallucination - Latency - Custom metrics 6. **Configure Trace Routing** - Tags - Span types - Model versioning - Prompt versioning **Developer rule:** > *Workflow assignment is just a structured binding of datasets, monitors, evaluators, and routing rules.* --- # 🔄 4. Ensure Correct Trace Routing (Critical for Custom Models) Your SDK must send traces with: ### **Required Fields** - `model_id` - `space_id` - `organization_id` - `project_id` (immutable once tracing is set up) - `tags` - `input` - `output` - `spans` - `metadata` ### **Optional but Recommended** - `prompt_id` - `prompt_version` - `model_version` - `evaluation_id` **Developer rule:** > *If the trace lacks the correct `space_id` or `project_id`, it will not enter the correct pipeline workflow.* --- # 🧱 5. Custom Model Registration Pattern (Developer Mental Model) Think of Arize AX as a **mesh of spaces**, each with its own pipeline. Your custom model must be registered like this: ``` Organization └── Space ├── Model ├── Datasets ├── Monitors ├── Evaluators ├── Prompts └── Experiments ``` If the model is placed in the wrong space, it inherits the wrong pipeline. **Developer rule:** > *Spaces are the pipeline boundary. Models inherit everything inside the space.* --- # 🛠️ 6. Developer Checklist for Correct Workflow Assignment ### **Before creating the model** - [ ] Confirm you are Space Admin or Space Member - [ ] Confirm the Space has the correct datasets - [ ] Confirm the Space has the correct monitors - [ ] Confirm the Space has the correct evaluators - [ ] Confirm the Space has the correct prompt groups ### **When creating the model** - [ ] Bind the model to the correct dataset - [ ] Enable the correct pipeline features - [ ] Attach evaluators - [ ] Attach monitors - [ ] Configure trace routing tags ### **When sending traces** - [ ] Include `space_id` - [ ] Include `project_id` - [ ] Include `model_id` - [ ] Include spans - [ ] Include metadata - [ ] Include tags for routing --- # 🧭 7. Developer-Friendly Description (One-Liner) > **A custom model’s pipeline workflow in Arize AX is determined entirely by the Space it lives in, the datasets it binds to, the monitors and evaluators attached to it, and the routing metadata included in its traces.** --- If you want, I can also generate: - A **code scaffold** for your SDK’s trace routing - A **diagram** showing pipeline inheritance - A **RBAC-safe multi-org deployment pattern** - A **custom role definition** for pipeline engineers - A **full workflow template** for your retail automation mesh Just tell me what direction you want to take this. Copilot said Tyler, I see what you’re doing — you’re taking the developer‑focused config I wrote and pasting it back as if to say: “Good. Now refine it, evolve it, sharpen it into the exact developer‑facing description I need.” So let’s do that. Below is a cleaner, tighter, more atomic developer‑facing description of correct pipeline workflow assignment for a custom model in Arize AX — written specifically for someone architecting modular, agent-aware SDKs and multi-space RBAC meshes. No fluff. No UI. Just the mental model + the pipeline contract + the routing rules. ⚙️ Developer-Focused Configuration Correct Pipeline Workflow Assignment for a Custom Model in Arize AX (Refined Developer Edition) 1. RBAC Determines Pipeline Boundaries A custom model’s workflow is defined by the Space it lives in. Spaces are the atomic pipeline units. RBAC determines what you can bind: Space Admin → full pipeline control Space Member → can bind datasets, monitors, evaluators Org Admin → controls integrations Org Member → can create spaces + integration keys Developer invariant: Pipeline correctness = model placed in the correct Space. 🧩 2. Pipeline Workflow = Structured Bindings A pipeline is not a “thing” — it’s a set of bindings: Dataset bindings Monitor bindings Evaluator bindings Prompt group bindings Routing metadata (tags, spans, versions) Your custom model must declare: Pipeline Bindings Primary dataset (input/output schema) Evaluation dataset (optional) Monitor set (drift, safety, quality, latency) Evaluator set (LLM-as-judge, custom scoring) Prompt group (if prompt-driven) Tags (routing selectors) Developer invariant: A model without dataset bindings cannot enter any pipeline. 🔐 3. Workflow Assignment = Binding the Model to the Space’s Pipeline Workflow assignment is deterministic: Model is created inside a Space Model binds to datasets inside that Space Model inherits monitors/evaluators inside that Space Trace routing determines which monitors/evaluators activate Model Settings → Workflow Assignment Bind: Input schema Output schema Span structure Metadata fields Monitor set Evaluator set Prompt group Routing tags Developer invariant: Workflow assignment = binding + inheritance + routing. 🔄 4. Trace Routing Is the Enforcement Layer Your SDK enforces pipeline correctness by sending traces with the right identifiers. Required Trace Fields model_id space_id organization_id project_id (immutable) input output spans metadata tags Optional but strongly recommended model_version prompt_id prompt_version evaluation_id Developer invariant: If space_id or project_id is wrong, the trace will not enter the correct workflow. 🧱 5. Custom Model Registration Pattern (Developer Mental Model) Arize AX is a mesh of Spaces. Each Space is a pipeline boundary. Code Copy Organization └── Space ├── Model ├── Datasets ├── Monitors ├── Evaluators ├── Prompts └── Experiments Models inherit everything inside the Space. Developer invariant: Spaces define pipeline scope. Models inherit pipeline components. 🛠️ 6. Developer Checklist (Atomic) Before model creation [ ] Space has correct datasets [ ] Space has correct monitors [ ] Space has correct evaluators [ ] Space has correct prompt groups [ ] You have Space Admin or Member permissions During model creation [ ] Bind primary dataset [ ] Bind evaluation dataset (optional) [ ] Attach monitors [ ] Attach evaluators [ ] Attach prompt group [ ] Configure routing tags During trace emission [ ] Include space_id [ ] Include project_id [ ] Include model_id [ ] Include spans [ ] Include metadata [ ] Include routing tags 🧭 7. Developer-Friendly One-Liner (Refined) A custom model’s pipeline workflow in Arize AX is determined entirely by the Space it lives in, the datasets it binds to, the monitors and evaluators attached to it, and the routing metadata included in its traces. Tyler, if you want, I can now generate: A pipeline contract you can embed directly into your SDK A routing schema for your agent mesh A multi-space deployment pattern for your retail automation system A model registration template you can hand to other developers A trace emission scaffold in TypeScript or Python Tell me which one moves your architecture forward. You said A pipeline contract you can embed directly into your SDK A routing schema for your agent mesh A multi-space deployment pattern for your retail automation system A model registration template you can hand to other developers A trace emission scaffold in TypeScript or Python Copilot said See my thinking You’re building a mesh—so let’s give you artifacts you can actually drop into it. 1. Pipeline contract to embed in your SDK This is the canonical contract your SDK enforces for any model that wants to participate in an Arize AX pipeline. PipelineContract (conceptual) Identity modelId: string spaceId: string organizationId: string projectId: string Schema inputSchema: JSON schema or typed struct outputSchema: JSON schema or typed struct spanSchema: definition of spans (steps, tools, agents) metadataSchema: key/value metadata contract Bindings primaryDatasetId: string evaluationDatasetId: string | null monitorIds: string[] evaluatorIds: string[] promptGroupId: string | null Routing tags: string[] modelVersion: string promptVersion: string | null Constraints invariants: spaceId must match the Space that owns primaryDatasetId projectId is immutable once tracing is set up modelId must be unique within spaceId You can encode this as a TypeScript interface or Python dataclass and make every agent register against it before emitting traces. 2. Routing schema for your agent mesh Think of this as how agents decide where traces go and which pipeline activates. Routing dimensions By space spaceId → pipeline boundary By project projectId → tracing lineage By model modelId, modelVersion → specific model instance By prompt promptId, promptVersion → prompt lineage By tags tags[] → feature flags, environment, tenant, workflow Example routing schema tags env:prod | env:staging | env:dev tenant:<tenant-id> workflow:<workflow-name> channel:web | channel:mobile | channel:kiosk agent:<agent-name> feature:<feature-flag> Routing rule mental model: Monitors, evaluators, and dashboards subscribe to combinations of spaceId, projectId, and tags. Your agents must emit traces with the right combination to land in the intended workflow. 3. Multi-space deployment pattern for retail automation You want a mesh that maps cleanly to business reality. Recommended structure Account Organization: RetailMesh Space: Core-Orchestration Models: router, planner, orchestrator Datasets: global routing logs Monitors: latency, failure rate, routing quality Space: Store-Front Models: customer-facing chat, kiosk assistant Datasets: customer interactions Monitors: CSAT, conversion, safety Space: Back-Office Models: inventory agent, pricing agent Datasets: inventory events, pricing decisions Monitors: stockout risk, margin impact Space: Experiments Models: experimental variants Datasets: A/B logs Monitors: experiment metrics Pattern: Use Spaces to separate: Core orchestration Customer-facing flows Operational flows Experiments/sandboxes Use tags to separate: Tenants Stores Regions Features 4. Model registration template for other developers This is what you hand to teammates as the “fill this out before you ship a model” doc. Model Registration Template Identity Model name: Model ID: Space: Organization: Project ID: Purpose Description: Primary use case: Owner (team/person): Schema Input schema (fields + types): Output schema (fields + types): Span structure (steps/tools): Metadata fields (keys + meaning): Bindings Primary dataset ID: Evaluation dataset ID (optional): Monitor IDs (drift, safety, quality, etc.): Evaluator IDs: Prompt group ID (if applicable): Routing Default tags: Model versioning strategy: Prompt versioning strategy (if applicable): RBAC Required role to manage this model (Space Admin/Member): Who can modify bindings: You can keep this as a YAML/JSON file in the repo and validate it against the pipeline contract. 5. Trace emission scaffold (TypeScript + Python) TypeScript scaffold ts Copy type TraceTag = string; interface TracePayload { model_id: string; space_id: string; organization_id: string; project_id: string; model_version?: string; prompt_id?: string; prompt_version?: string; evaluation_id?: string; input: unknown; output: unknown; spans: unknown[]; metadata: Record<string, unknown>; tags: TraceTag[]; } function emitTrace(payload: TracePayload) { // Invariants if (!payload.model_id || !payload.space_id || !payload.project_id) { throw new Error("Missing required identity fields (model_id, space_id, project_id)."); } // TODO: add your HTTP client / SDK call here // e.g. POST to Arize AX tracing endpoint } Python scaffold python Copy from dataclasses import dataclass from typing import Any, Dict, List, Optional @dataclass class TracePayload: model_id: str space_id: str organization_id: str project_id: str model_version: Optional[str] = None prompt_id: Optional[str] = None prompt_version: Optional[str] = None evaluation_id: Optional[str] = None input: Any = None output: Any = None spans: List[Any] = None metadata: Dict[str, Any] = None tags: List[str] = None def emit_trace(payload: TracePayload) -> None: if not payload.model_id or not payload.space_id or not payload.project_id: raise ValueError("Missing required identity fields (model_id, space_id, project_id).") # TODO: add your HTTP client / SDK call here # Example: # requests.post(ARIZE_TRACE_URL, json=payload.__dict__) If you want, next step we can: Turn this into a repo-ready spec (README + contracts + examples), or Map it directly onto your current Arize AX space/project setup so it’s not just abstract but live.