--- title: Tool Broker Pattern | Decision Boundaries AI Agents description: Decision boundaries AI agents require a Tool Broker not direct tool access. 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View Plans → [Get Demo](https://www.elixirdata.co/context-os/demo/) [LLMS TXT](https://www.elixirdata.co/llms.txt) [LLMS Full TXT](https://www.elixirdata.co/llms-full.txt) [AI Context JSON](https://www.elixirdata.co/ai-context.json) [Decision Infrastructure](https://www.elixirdata.co/blog/tag/decision-infrastructure) # Tool Broker Pattern | Decision Boundaries AI Agents [Dr. Jagreet Kaur Gill](https://www.elixirdata.co/blog/author/dr-jagreet-kaur-gill) | 30 September 2026 Tool Broker Pattern | Decision Boundaries AI Agents 27:03 ### Key Takeaways - Direct tool access in [**Agentic AI**](https://www.xenonstack.com/agentic-ai/)systems creates the same governance gap that unprotected microservice access creates in distributed architectures — and the solution is structurally identical. - The **Tool Broker pattern** sits between **AI agents** and tools, enforcing schema validation, scope enforcement, secrets management, egress control, and redaction at every tool call. - **Decision boundaries AI agents** require are not optional policies — they are architectural controls that prevent unauthorized tool execution before it reaches the tool layer. - [**AI agent reliability**](https://www.elixirdata.co/blog/ai-agent-reliability) depends on governed execution: without a Tool Broker, agents inherit whatever permissions the tool has — with no isolation between agent scope and tool capability. - The Tool Broker generates audit-grade [**AI agent decision tracing**](https://www.elixirdata.co/blog/ai-agent-decision-tracing-vs-telemetry) records — capturing not just what happened, but whether it was authorized, what policy was evaluated, and what context governed the call. - [**Context OS**](https://www.elixirdata.co/product/context-os/) implements the Tool Broker pattern as a core layer of the [**Governed Agent Runtime**](https://www.elixirdata.co/blog/what-is-a-governed-agent-runtime-category-definition-architecture) — the execution environment that makes [**governed agentic execution**](https://www.elixirdata.co/blog/governed-agentic-execution) possible in production enterprise systems. - Unlike [**LangChain vs CrewAI vs Context OS**](https://www.elixirdata.co/blog/langchain-vs-crewai-vs-context-os-for-ai-agent-governance) comparisons that focus on orchestration capability, the Tool Broker addresses the governance gap that no orchestration framework closes. - The Tool Broker is the **AI agent evaluation framework** control point — the architectural location where [**AI agent guardrails vs governance**](https://www.elixirdata.co/blog/ai-agent-guardrails-vs-governance) resolves from debate into enforcement. ## [![CTA 2-Jan-05-2026-04-30-18-2527-AM](https://www.elixirdata.co/hs-fs/hubfs/CTA%202-Jan-05-2026-04-30-18-2527-AM.webp?width=871&height=132&name=CTA%202-Jan-05-2026-04-30-18-2527-AM.webp)](https://www.elixirdata.co/) ## The Tool Broker Pattern: Why AI Agents Should Never Call Tools Directly In most [**Agentic AI**](https://www.xenonstack.com/agentic-ai/) deployments today, agent frameworks give agents direct access to tools. The agent decides to call a function, and the function executes. No intermediary. No validation beyond the function's own input parsing. No record beyond the framework's trace. No enforcement of **decision boundaries AI agents** require before actions execute. This is the architectural equivalent of giving every microservice direct write access to every database in your infrastructure. Enterprise engineering stopped doing that years ago. The industry introduced API gateways, service meshes, and access control layers — recognizing that direct access creates security, reliability, and auditability risks that outweigh the simplicity benefit. **AI agents need the same evolution.** Direct tool access needs to be replaced with brokered execution. This article defines the Tool Broker pattern, explains what it enforces, and positions it within the broader architecture of [**Decision Infrastructure**](https://www.elixirdata.co/platform/decision-infrastructure/) and the [**Context OS**](https://www.elixirdata.co/product/context-os/) [Governed Agent Runtime.](https://www.elixirdata.co/blog/what-is-a-governed-agent-runtime-category-definition-architecture) ## What Is the Governance Gap in Direct Tool Access for AI Agents? *Most agent deployments today have no policy evaluation at execution time. Understanding this gap is the first step to closing it.* When an **AI agent** calls a tool directly, the framework evaluates the agent's intent and routes the call. But nothing evaluates whether the agent is *authorized* to make this specific call with these specific parameters at this specific moment. The policy gap between "the agent decided to call this tool" and "this tool call is authorized" is where production failures live. Three structural problems emerge from direct tool access in [**Agentic AI**](https://www.xenonstack.com/agentic-ai/) systems: **1. No Policy at Execution Time** When an agent calls a tool directly, the framework evaluates intent and routes the call — but no layer evaluates authorization. There are no **decision boundaries AI agents** can be checked against at the moment of execution. The absence of runtime policy enforcement is not a configuration gap; it is an architectural gap. **2. No Isolation Between Agent Scope and Tool Capability** Direct tool access means the agent has whatever permissions the tool has. If the tool has database write access, the agent has database write access. If the tool can access multiple tenants' data, the agent can access multiple tenants' data. There is no isolation boundary. [**AI agent reliability**](https://www.elixirdata.co/blog/ai-agent-reliability) in multi-tenant or regulated environments cannot be achieved without scope isolation. **3. No Audit-Grade Decision Trace** The framework may log that a tool was called. But the log does not capture whether the call was authorized, what policy was evaluated, what parameters were validated, or what the complete execution context was. It records what happened — not whether it should have happened. This is the difference between a framework trace and [**AI agent decision tracing**](https://www.elixirdata.co/blog/ai-agent-decision-tracing) that satisfies governance and audit requirements. These three gaps explain why enterprise AI initiatives that move from experimentation to production encounter governance failures that no amount of prompt engineering or orchestration refinement resolves. The failure is structural. ## What Is the Tool Broker Pattern and How Does It Work in Agentic AI Systems? *The Tool Broker is the governed execution layer that sits between AI agents and tools — enforcing policy, isolation, and evidence generation at every call.* A **Tool Broker** is a controlled execution intermediary that sits between **AI agents** and the tools they call. It does not replace the tool or the agent. It governs the execution path between them. When an agent needs to call a tool, the request goes to the Tool Broker — not to the tool directly. The broker then executes the following sequence before, during, and after every tool call: 1. Validates the request against the agent's scoped permissions 2. Checks parameters against policy constraints — thresholds, formats, allowed values 3. Enforces rate limits and budget constraints 4. Applies the staged commit protocol: preflight → diff → approve → commit 5. Generates the idempotency key 6. Executes the tool call within an isolation boundary 7. Captures input, output, timing, and any errors 8. Writes the execution record to the Decision Trace in [**Decision Infrastructure**](https://www.elixirdata.co/platform/decision-infrastructure/) The Tool Broker does not add latency as a side effect — it adds governance as a first-class architectural property. Every tool call produces a structured record that answers four governance questions: Was this call authorized? Were the parameters valid? Was the execution isolated? What exactly happened? ## What Does a Tool Broker Enforce? The Five Governance Controls for Governed Agentic Execution *Governed agentic execution requires five specific enforcement controls at the tool call layer. Each resolves a distinct failure mode in direct tool access architectures.* The Tool Broker enforces five controls that, taken together, constitute **governed agentic execution** for enterprise **AI agents**: | **Control** | **What It Enforces** | **Failure Mode It Prevents** | **Direct Tool Access** | | --- | --- | --- | --- | | **Schema Validation** | Every tool call validated against defined schema before execution. Incorrect types, ranges, or formats rejected before reaching the tool. | Malformed parameters reaching tool execution layer | Not enforced | | **Scope Enforcement** | Checks whether the agent has permission to call this tool, with these parameters, for this task. Purpose-bound permissions enforced at every call. | Agent inheriting tool's full permission set | Not enforced | | **Secrets Management** | Broker manages credential scoping. Agent never sees actual credentials. Appropriate credentials attached within isolation boundary. | Credential exposure in agent context window or logs | Not enforced | | **Egress Control** | Controls what external systems the tool can reach. Agent calls cannot be redirected to unauthorized external APIs or data destinations. | Tool call redirected to unauthorized external system | Not enforced | | **Decision Trace Redaction** | Sensitive fields in tool inputs and outputs redacted in Decision Trace. Schema declares sensitive fields. Audit trail captures what happened without exposing PII or credentials. | PII or credential leakage in audit records | Not enforced | Each of these controls resolves a specific instance of the broader [**AI agent guardrails vs governance**](https://www.elixirdata.co/blog/ai-agent-guardrails-vs-governance) question. Guardrails are reactive constraints applied after the agent has decided to act. Governance is proactive enforcement applied before the tool call executes. The Tool Broker operationalizes governance — not guardrails — as the architectural model. ## How Does AI Agent Decision Tracing Work Through the Tool Broker? *AI agent decision tracing through the Tool Broker produces audit-grade execution records — not framework logs. The distinction matters for governance, compliance, and Decision Infrastructure.* Framework logs record that a tool was called. [**AI agent decision tracing**](https://www.elixirdata.co/blog/ai-agent-decision-tracing) through the Tool Broker records whether the call was authorized, what policy governed it, and whether the outcome was consistent with the agent's declared purpose. Every Tool Broker execution produces a structured Decision Trace containing: - **Tool call identity** — which tool, which agent, which task, which timestamp - **Authorization record** — which policy was evaluated, what the evaluation result was, what permission scope applied - **Parameter validation record** — what schema was checked, what values were submitted, what was accepted or rejected - **Execution context** — what rate limits were applied, what budget constraints were enforced, what idempotency key was assigned - **Isolation boundary record** — what egress was permitted, what credentials were scoped, what external systems were accessible - **Input and output** — complete record of what went in and what came out, with sensitive fields redacted per schema declaration - **Error and escalation record** — if the call was blocked, modified, or escalated, what triggered that action state These Decision Traces accumulate in the [**Decision Infrastructure**](https://www.elixirdata.co/blog/decision-infrastructure-the-foundation-of-decision-intelligence) Decision Ledger — the governed record of every tool call every agent made, under what authorization, with what outcome. This is the audit trail that enterprise governance, risk, and compliance functions require. It is also the dataset the [**AI agent evaluation framework**](https://www.elixirdata.co/blog/ai-agent-evaluation-framework-decision-governance) uses to assess agent behavior at scale. ## How Does the Tool Broker Pattern Compare to the API Gateway in Microservice Architecture? *The Tool Broker is the API gateway for Agentic AI — the same architectural reasoning, applied to agent actions instead of service calls.* The analogy is precise. An API gateway sits between clients and services, enforcing authentication, rate limiting, request validation, and logging. A Tool Broker sits between **AI agents** and tools, enforcing identity, policy, execution control, and evidence generation. | **Architectural Layer** | **Microservice Architecture** | **Agentic AI Architecture** | | --- | --- | --- | | **Control Layer** | API Gateway | Tool Broker | | **What It Governs** | Service-to-service calls | Agent-to-tool calls | | **Identity Enforcement** | OAuth / JWT / API key | Agent identity + purpose-bound scope | | **Request Validation** | Schema / contract validation | Tool schema + parameter policy validation | | **Rate / Budget Control** | Rate limiting per client / route | Rate limits + token / cost budget per agent task | | **Secrets Handling** | Secrets manager injection at gateway | Credential scoping within isolation boundary | | **Audit Record** | Access log with request / response | Decision Trace with authorization, policy, execution context | | **Direct Access Risk** | Service has full DB write access | Agent inherits full tool permission set | The same arguments that justified API gateways justify the Tool Broker. You cannot govern what you do not control. Direct access prevents control. The enterprise infrastructure industry learned this with microservices. The **AI agents computing platform** industry is learning it with agentic systems — and the architectural answer is structurally identical. ## How Does LangChain vs CrewAI vs Context OS Resolve the Tool Broker Gap? *The distinction between orchestration frameworks and a [governed agent runtime](https://www.elixirdata.co/blog/what-is-a-governed-agent-runtime-category-definition-architecture) is the architecture gap that the LangChain vs CrewAI vs Context OS question actually surfaces.* When enterprise teams evaluate **LangChain vs CrewAI vs Context OS**, they often frame the comparison as orchestration capability — workflow coordination, multi-agent patterns, tool integration. This framing misses the architectural distinction that matters for production deployments. LangChain and CrewAI are orchestration frameworks. They coordinate how agents call tools in sequence or in parallel. They do not govern *whether* agents should call a specific tool with specific parameters under specific conditions. Governance is outside their architectural scope. **Context OS** is a **Context OS** — a governed execution environment built on **Decision Infrastructure**. The [Governed Agent Runtime](https://www.elixirdata.co/blog/what-is-a-governed-agent-runtime-category-definition-architecture) within Context OS implements the Tool Broker pattern natively. Every tool call executed within Context OS passes through the Tool Broker before it reaches the tool layer. The architectural position is additive, not competitive: - **LangChain / CrewAI** — orchestration layer: how agents coordinate and execute workflows - [**Context OS Governed Agent Runtime**](https://www.elixirdata.co/blog/what-is-a-governed-agent-runtime-category-definition-architecture) — governance layer: whether each agent action is authorized, isolated, and traced before execution - **Tool Broker** — execution control point: the specific component that enforces governance at the moment of tool call Governed agentic execution does not require replacing orchestration frameworks. It requires adding the governance layer that orchestration frameworks do not provide. The Tool Broker is that layer's execution control point. ## Why Is the Tool Broker the Enterprise AI Agent Evaluation Framework Control Point? *Enterprise AI agent evaluation requires a governed execution record. The Tool Broker is the only architectural layer that generates this record at the tool call level.* An [**AI agent evaluation framework**](https://www.elixirdata.co/blog/ai-agent-evaluation-framework-decision-governance) for enterprise deployments must answer questions that no model benchmark or orchestration trace resolves: - Did the agent call the correct tools with the correct parameters for the given task? - Did the agent respect the authorization boundaries defined for its scope? - Was there a consistent pattern between agent decisions and policy constraints? - When the agent encountered an edge case, did it escalate correctly or proceed autonomously? - Is the agent's tool usage behavior consistent across similar inputs, or does it vary in ways that indicate reliability risk? None of these questions can be answered from model benchmarks or framework traces. They require a governed execution record that captures authorization context, policy evaluation, and action state per tool call. The Tool Broker is the architectural component that produces this record. [**AI agent reliability**](https://www.elixirdata.co/blog/ai-agent-reliability)in enterprise systems is not primarily a model quality problem. It is a governance infrastructure problem. Agents fail in production because they lack **decision boundaries** that enforce consistent behavior — not because the underlying model is inadequate. The Tool Broker enforces those decision boundaries at the execution layer. ## How Does Context OS Implement the Tool Broker as Governed Agent Runtime? *Context OS operationalizes the Tool Broker pattern as a native component of the [Governed Agent Runtime](https://www.elixirdata.co/blog/what-is-a-governed-agent-runtime-category-definition-architecture)— the execution environment that makes governed agentic execution architecturally guaranteed, not optionally configured.* - **Context OS** is ElixirData's **AI agents computing platform** — the governed operating system for enterprise [**Agentic AI**](https://www.xenonstack.com/agentic-ai/) systems. The Governed Agent Runtime is Context OS's execution environment for AI agents, built around three core components that implement the Tool Broker pattern in production: - **Decision Boundaries** are policies encoded as executable constraints. Before any tool call executes, the [Governed Agent Runtime](https://www.elixirdata.co/blog/what-is-a-governed-agent-runtime-category-definition-architecture) evaluates the proposed action against all applicable Decision Boundaries. If the call violates a boundary, the action state is set — Allow, Modify, Escalate, or Block — before the tool is reached. This is how **decision boundaries AI agents** require become architectural enforcement rather than configuration guidelines. - [**Decision Traces**](https://www.elixirdata.co/platform/decisiontraces/) are the structured execution records generated at every governed tool call. Every Decision Trace captures the input state, the boundaries evaluated, the boundary results, the action state, the action detail, and the evidence supporting the decision. These traces accumulate in the Decision Ledger, forming the governed execution record that audit, compliance, and evaluation functions require. - [**The Decision Flywheel**](https://www.elixirdata.co/blog/decision-flywheel-ai)uses accumulated Decision Traces to continuously calibrate agent behavior. As Decision Traces accumulate, the system learns which tool call patterns consistently produce reliable outcomes — and which patterns correlate with boundary violations or escalations. [**AI agent decision tracing**](https://www.elixirdata.co/blog/ai-agent-decision-tracing) through the Tool Broker is not just a governance record; it is the training signal for continuously improving agent reliability. Together, these components make [**governed agentic execution**](https://www.elixirdata.co/blog/governed-agentic-execution) an architectural property of Context OS — not a runtime configuration that can be bypassed when execution velocity is prioritized over governance. ## Conclusion: Governed Agentic Execution Starts at the Execution Layer Direct tool access in [**Agentic AI**](https://www.xenonstack.com/agentic-ai/) systems is the governance gap that enterprise deployments have not yet systematically closed. The argument for closing it is not about caution — it is about architecture. The same reasoning that eliminated direct service-to-database access in microservice systems applies to direct agent-to-tool access in agentic systems. The architectural answer is structurally identical: introduce a control layer that enforces policy, isolation, and evidence generation at every execution boundary. The Tool Broker pattern provides this control layer. It enforces **decision boundaries AI agents** require, produces the **AI agent decision tracing** records that governance and evaluation demand, and resolves the [**AI agent guardrails vs governance**](https://www.elixirdata.co/blog/ai-agent-guardrails-vs-governance) question in favor of architectural governance — not reactive output constraints. **AI agent reliability** is not a model quality problem. It is a governance infrastructure problem. Agents that call tools without authorization enforcement, scope isolation, and audit-grade tracing are not reliable — regardless of the orchestration framework or model quality. The Tool Broker is the architectural component that makes reliability achievable. [**Context OS**](https://www.elixirdata.co/) implements the Tool Broker as the native execution control layer of the [Governed Agent Runtime](https://www.elixirdata.co/blog/what-is-a-governed-agent-runtime-category-definition-architecture) — the **AI agents computing platform** that makes **governed agentic execution** the default, not the exception, for enterprise **Agentic AI** deployments. **Agents should never call tools directly. The Tool Broker pattern provides the control layer that makes agent execution governed, isolated, and auditable.** ## [![CTA-Jan-05-2026-04-28-32-0648-AM](https://www.elixirdata.co/hs-fs/hubfs/CTA-Jan-05-2026-04-28-32-0648-AM.jpeg?width=858&height=130&name=CTA-Jan-05-2026-04-28-32-0648-AM.jpeg)](https://demo.elixirdata.co/) ## Frequently Asked Questions 1. ### **What is the Tool Broker pattern in Agentic AI?** The Tool Broker pattern is an architectural design in which a governed execution intermediary sits between AI agents and the tools they call. The broker enforces policy, validates parameters, manages credentials, controls egress, and generates audit-grade Decision Traces before any tool call executes against real systems. 2. ### **What are decision boundaries in AI agents?** Decision boundaries are policies encoded as executable constraints within the [Governed Agent Runtime](https://www.elixirdata.co/blog/what-is-a-governed-agent-runtime-category-definition-architecture). Before any agent action executes, the runtime evaluates the proposed action against all applicable decision boundaries and determines an action state: Allow, Modify, Escalate, or Block. Decision boundaries enforce governed agentic execution at the architectural level. 3. ### **How does the Tool Broker improve AI agent reliability?** AI agent reliability requires consistent governed decisions, scope isolation, and audit-grade traceability. The Tool Broker enforces all three at every tool call. It prevents agents from executing unauthorized actions, isolates agent scope from tool capability, and generates Decision Traces that enable continuous agent evaluation and behavioral calibration through the Decision Flywheel. 4. ### **What is the difference between AI agent guardrails and AI agent governance?** Guardrails are reactive output constraints applied after the model generates a response. Governance is proactive enforcement applied before an agent action executes against real systems. The Tool Broker implements governance — the enforcement point is the execution layer, not the output layer. Guardrails constrain what the agent says; governance constrains what the agent does. 5. ### **How does AI agent decision tracing differ from framework tracing in LangSmith or Langfuse?** Framework traces record execution flow — what ran, in what order, with what latency. [Decision Traces](https://www.elixirdata.co/platform/decisiontraces/)record governance — was the action authorized, what policy applied, what was the decision rationale, what was the action state. They answer different questions and serve different enterprise functions. Decision Traces are the governance record. Framework traces are the operational record. 6. ### **Does Context OS replace LangChain or CrewAI?** No. Context OS provides the governance layer that orchestration frameworks do not. When evaluating [LangChain vs CrewAI vs Context OS](https://www.elixirdata.co/blog/langchain-vs-crewai-vs-context-os-for-ai-agent-governance), the distinction is: LangChain and CrewAI handle orchestration — workflow coordination and tool integration. Context OS provides the Governed Agent Runtime that enforces decision boundaries, generates Decision Traces, and implements the Tool Broker at every tool execution boundary. 7. ### **What is governed agentic execution?** Governed agentic execution is the operational model in which every AI agent action — including every tool call — is evaluated against declared policies, executed within an isolation boundary, and recorded in an audit-grade Decision Trace before consequences propagate to real systems. It is implemented through the [Governed Agent Runtime](https://www.elixirdata.co/blog/what-is-a-governed-agent-runtime-category-definition-architecture)within Context OS. --- ## Further Reading - [Governed Agent Runtime — The Complete Guide](https://www.elixirdata.co/blog/what-is-a-governed-agent-runtime-category-definition-architecture) - [Context OS — The Context Platform for Agentic Enterprises](https://www.elixirdata.co/product/context-os/) - [Decision Intelligence — Decision Infrastructure for Agentic Enterprises](https://www.elixirdata.co/blog/decision-intelligence-infrastructure) - [AI Agent Reliability: Decision Consistency, Not Uptime](https://www.elixirdata.co/blog/ai-agent-reliability) - [What Is an AI Agent Evaluation Framework? The Enterprise Guide](https://www.elixirdata.co/blog/ai-agent-evaluation-framework-decision-governance) ## Share Article - [![XenonStack Facebook](https://www.xenonstack.com/hubfs/xenonstack-facebook-service.svg)](http://www.facebook.com/share.php?u=https://www.elixirdata.co/blog/tool-broker-pattern-decision-boundaries-ai-agents) - [![XenonStack Twitter](https://www.xenonstack.com/hubfs/xs-twitter-white-updated-icon.svg)](https://twitter.com/intent/tweet?text=I+found+this+interesting+blog+post&url=https://www.elixirdata.co/blog/tool-broker-pattern-decision-boundaries-ai-agents) - [![XenonStack Linked In](https://www.xenonstack.com/hubfs/xenonstack-linkedin-service.svg)](http://www.linkedin.com/shareArticle?mini=true&url=https://www.elixirdata.co/blog/tool-broker-pattern-decision-boundaries-ai-agents) - [![XenonStack Email Icon](https://www.xenonstack.com/hubfs/xenonstack-email-service.svg)](mailto:?subject=Check%20out%20https://www.elixirdata.co/blog/tool-broker-pattern-decision-boundaries-ai-agents%20&body=Check%20out%20https://www.elixirdata.co/blog/tool-broker-pattern-decision-boundaries-ai-agents) ## Table of Contents ## Explore Related Topics [Agentic Operations](https://www.elixirdata.co/blog/tag/agentic-operations) [Context Application](https://www.elixirdata.co/blog/tag/context-application) [Context Graph](https://www.elixirdata.co/blog/tag/context-graph) [Context OS](https://www.elixirdata.co/blog/tag/context-os) [Decision Graph](https://www.elixirdata.co/blog/tag/decision-graph) [Knowledge Graph](https://www.elixirdata.co/blog/tag/knowledge-graph) [Ontology](https://www.elixirdata.co/blog/tag/ontology) ![dr-jagreet-gill](https://www.elixirdata.co/hubfs/Imported%20images/dr-jagreet-gill-author.svg) ## Dr. Jagreet Kaur Gill Chief Research Officer and Head of AI and Quantum Dr. Jagreet Kaur Gill specializing in Generative AI for synthetic data, Conversational AI, and Intelligent Document Processing. With a focus on responsible AI frameworks, compliance, and data governance, she drives innovation and transparency in AI implementation [Explore More by Dr. Jagreet Kaur Gill ![cta-blue-arrow](https://www.elixirdata.co/hubfs/Imported%20images/cta-arrow-blue.svg)](https://www.elixirdata.co/blog/author/dr-jagreet-kaur-gill) ## Subscribe to our Latest Technology Insights and Resources Subscribe Now ![slider-cross-icon](https://www.xenonstack.com/hubfs/slider-cross-icon.svg) ## Get the latest articles in your inbox Business Email ID \* Please enter a valid Business Email ID Company Name \* Please enter a valid Company Name Yes, I would like to receive the ElixirData newsletter as well as marketing emails regarding ElixirData products, services, and events. I understand I can unsubscribe at any time. By registering, I confirm that I agree to the processing of my personal data by ElixirData as described in the Privacy Policy. 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