Introduction
Getting an AI agent to work in a demo is the easy part. But, the hard part is to make it reliable enough to ship to real users. That's where most developers face the same issues when they try to move agents into production.
You build a prototype that handles customer support or automates workflows, and it works great locally. Then you deploy it, and the agent starts making strange decisions, calling the wrong tools, or burning through tokens. This happens because limited testing before shipping doesn't prepare the agent for the unpredictable nature of real-world usage. The core issue isn't your code; it's a visibility problem. You can't see inside the agent's reasoning process because you don't know what's breaking in your multi-agent flow.
OpenAI launched AgentKit on October 6, 2025 to handle orchestration of multi agentic flows. It gives developers a visual workflow builder, embeddable UI components, and deployment tools. But that's only half the stack because you need to evaluate and continuously monitor how its performing.
Future AGI provides the reliability layer. It automatically traces every agent interaction, logs tool calls and handoffs, and runs continuous evaluations on live traffic.
Together, they form an end-to-end stack from prototype to production. This guide walks through how AgentKit and Future AGI work together technically and how to set up the integration.
OpenAI AgentKit: Architecture and Core Components
AgentKit gives developers a modular toolkit for the complete agent development lifecycle. It packages a visual Agent Builder, embeddable ChatKit UI, Connector Registry, and Agents SDK into a single platform for shipping production agents.
Image 1: AgentKit: Source
2.1 Agent Builder
Agent Builder is a visual canvas for designing, versioning, and debugging multi-agent workflows using drag-and-drop nodes. OpenAI's CEO describes it as "Canva for building agents" because it compresses what used to take months of custom code into a few hours.
Technical Deep Dive
The visual graph translates directly to state machines that execute through OpenAI's Responses API. Each node on the canvas represents a state, and the connections between nodes define transitions, agent handoffs, and parallel task execution.
The canvas supports conditional branching with If/Else nodes, loops with While nodes, and human-in-the-loop flows with User Approval nodes.
Multi-agent workflows run in parallel when nodes are disconnected from the main execution path, while sequential tasks follow the connected graph structure.
Built-in versioning tracks changes to the workflow graph, and inline eval configuration lets developers test runs directly on the canvas before deployment.
2.2 Connector Registry
Connector Registry is a centralized service for managing secure access to data sources, APIs, databases, and external tools. It reduces glue code by providing built-in connectors for web search, file search, image generation, code interpreter, and Model Context Protocol (MCP) servers.
Technical Deep Dive
The registry enforces least-privilege permissions by scoping each connector to specific agent workflows. It integrates with enterprise authentication systems through OAuth flows and API key management, ensuring agents only access authorized resources.
Each connector in the registry maintains its own authentication credentials and access scope, preventing agents from making unauthorized API calls.
MCP support allows agents to connect to third-party services without writing custom integration code.
The registry acts as a central audit point for all external tool calls, making it easier to track which agents access which resources.
2.3 ChatKit
ChatKit is a frontend toolkit for embedding customizable, chat-based agent experiences into applications. It handles streaming responses, thread management, and brand-consistent UI elements that typically take weeks to build from scratch.
Technical Deep Dive
The ChatKit API streams responses from the agent in real time, showing users the agent's chain-of-thought reasoning and live tool usage. Developers can customize UI components to match their brand while keeping the core interaction logic managed by OpenAI.
ChatKit sessions connect to a backend endpoint that can run on OpenAI's hosted infrastructure or a custom server using the ChatKit Python SDK.
The UI automatically handles file and image uploads, thread persistence across sessions, and displays thinking states when the agent is processing.
Developers can override default styles, add custom buttons, or inject middleware to transform messages before they reach the agent.
2.4 Agents SDK (Python/TypeScript)
The Agents SDK provides programmatic control over agent definition, tool creation, and orchestration logic for developers who need code-first workflows. It shares the same execution substrate as Agent Builder (the Responses API) but gives full control over every component.
Use Case Comparison
Use Agent Builder for rapid prototyping, testing multi-agent handoffs visually, or when non-technical team members need to collaborate on workflow design. Use the Agents SDK when building complex custom logic, integrating with existing codebases, or when version control and CI/CD pipelines require treating agent definitions as code.
Agent Builder works best for customer support bots, internal automation agents, and domain-specific assistants where workflows follow predictable patterns.
The SDK fits use cases like multi-step research agents with dynamic branching, agents that need custom tool implementations, or systems where agent logic must be tested programmatically.
Developers can start with Agent Builder and export the workflow to TypeScript or Python code for further customization.
2.5 Evals: Built-In Performance Testing
AgentKit includes four evaluation capabilities to test and improve agent performance before deployment. Developers can create datasets to build test scenarios from scratch, use trace grading to run end-to-end assessments of workflow quality, apply automated prompt optimization to generate better prompts based on test results, and evaluate third-party models on the same platform. These tools work well for rapid iteration and catching obvious issues during development.
Future AGI provides all this. The platform offers deep production observability, custom evaluation metrics, automated optimization recommendations, real-time monitoring dashboards, and safety filtering capabilities that AgentKit's built-in evaluation tools don't cover.
Future AGI: The Evaluation and Optimization Layer
Future AGI is an evaluation and optimization platform that helps developers achieve high accuracy in their AI applications. It provides a reliability layer with features like multimodal evaluation, custom metrics, observability, and specialized tools for comparing agent performance. When you're ready to move from prototype to production, the platform's evaluation tools help you tune models for better accuracy and real-world performance.
3.1 Auto-Instrumentation
Future AGI's auto-instrumentation library turns opaque agent behavior into a transparent, analyzable process with minimal setup. You can integrate it by installing the SDK and adding a short code snippet to your project, which provides full observability and performance tracing without extra steps.
Installation: Install the library directly into your environment using
pip install futureagi.Configuration: Set your Future AGI API and secret keys as environment variables in your project.
Integration: Copy the telemetry snippet into your code before any other execution begins. The library offers plug-and-play instrumentation for major frameworks like the OpenAI Agent SDK.
3.2 Datasets Module: Synthetic Data Generation
The Datasets module generates high-quality synthetic data, including edge cases and adversarial examples, to test agent robustness. By connecting to a knowledge base, it creates grounded, context-aware datasets that ensure agents are tested against realistic scenarios and can scale to thousands of examples from just a few user-provided samples.
Edge Case Generation: The platform automatically creates diverse training and test data, which is useful for specialized applications like Retrieval-Augmented Generation (RAG).
Knowledge Base Integration: You can define data requirements or provide a few examples, and Future AGI scales the generation to create large, customized datasets quickly.
Iterative Refinement: It automatically checks and improves data quality using semantic and distribution tests.
Image 2: Dataset Module
3.3 Experiment Module: Systematic Configuration Testing
The Experiment module offers a no-code A/B testing framework for systematically comparing different agent configurations, prompts, and models like GPT-5 and Claude 3. It provides side-by-side comparisons of metrics such as accuracy, latency, and cost to help developers identify the optimal setup.
No-Code A/B Testing: A visual interface allows for multi-variant experiments without writing complex testing scripts.
Side-by-Side Metrics: The module tracks and compares performance across experiments, highlighting a "winner" based on built-in or custom evaluation metrics.
Configuration Comparison: Teams can test different prompts, models, and orchestration strategies to find the best-performing combination for their specific use case.
Image 3: Experiment Module
3.4 Evaluate Module: Custom Metric Assessment
The Evaluate module uses proprietary evaluation metrics and supports custom graders to assess agent performance on multiple fronts. This allows for deep assessments of factual accuracy, toxicity, PII detection, relevance, and other domain-specific metrics that go beyond simple pass/fail checks.
Proprietary Metrics: Future AGI provides built-in metrics for a range of use cases, including RAG and text-to-image generation.
Custom Graders: Developers can define their own evaluation criteria to measure performance against specific business goals or quality standards.
In-Depth Analysis: The module can pinpoint which parts of an input cause an agent to fail a specific evaluation, making the results more interpretable.
Image 4: Evaluate Module
3.5 Improve Module: Automated Optimization
The Improve module creates a closed-loop feedback system where evaluation insights automatically generate optimization recommendations for underperforming agents. It can identify flawed prompts or incorrect tool usage patterns and automatically refine them based on evaluation results or custom inputs.
Feedback Loop: The system uses feedback from evaluations to automatically refine and improve an application's prompts.
Prompt Refinement: It helps developers manage versioned prompt suites and run tests to ensure prompt changes lead to better performance.
Workflow Optimization: The platform identifies and suggests improvements for agent workflows to enhance overall performance and accuracy.
Image 5: Improve Module
3.6 Monitor & Protect Module: Production Observability
The Monitor & Protect module delivers real-time observability into production applications with dashboards for tracking throughput, error rates, and latency. It includes smart alerting for performance degradation and safety metrics that filter and block unsafe content with minimal latency.
Real-Time Dashboards: Track key performance indicators in production to diagnose issues and maintain application health.
Smart Alerting: Receive notifications for performance drops, quality issues, or security threats detected in live traffic.
Content Filtering: Use the Protect feature to scan and filter responses in real time, blocking harmful or inappropriate content based on predefined rules like toxicity or sexism.
Image 6: Monitor & Protect Module
3.7 Multimodal Evaluation: Beyond Text
Future AGI supports the evaluation of AI systems across text, image, audio, and video, making it suitable for modern multimodal agent systems. The platform can pinpoint errors in any modality and automatically generate feedback to improve performance.
Cross-Modality Support: The platform enables evaluation across different modalities with both built-in and custom metrics.
Deep Evaluations: It performs deep assessments of text, image, audio, and video models to uncover performance challenges that single-modality tools might miss.
Actionable Feedback: After identifying errors in any modality, the system provides feedback to help improve the model or agent workflow.
The End-to-End Workflow: From Prototype to Production
Step 1: Design & Build with OpenAI AgentKit
Prototype a multi-agent system using Agent Builder, such as a "Sales Research Agent" that finds leads and a "Content Agent" that drafts personalized outreach emails.
Technical Details:
Define agent roles, prompts, and tools each can access via the Connector Registry. For example, give the Research Agent access to web search and database connectors to pull lead information, while the Content Agent gets access to file search for past email templates.
Map out the workflow graph, detailing the handoff points where the Research Agent passes structured data (lead name, company, pain points) to the Content Agent. Use shared state variables like
lead_data.nameandlead_data.companyto ensure smooth transitions between agents.
Step 2: Instrument & Observe with Future AGI
Integrate Future AGI's auto-instrumentation with the agent's underlying code using the Agents SDK configuration.
Technical Details:
Here's the setup code:
|
Future AGI captures end-to-end traces automatically once instrumentation is set up. This includes initial prompts, tool calls with parameters, LLM token usage, latency breakdowns, and agent-to-agent data transfers, giving full visibility into the workflow without adding manual logging.
Step 3: Evaluate & Benchmark for Accuracy
Use both AgentKit's native tools and Future AGI's advanced suite to measure performance across different dimensions.
Technical Comparison:
AgentKit Evals: Good for initial validation during development, offering trace grading to check if agents followed logical steps and automated prompt optimization based on performance results. The built-in dataset creation feature helps teams build test cases quickly, though it requires manual construction of expected outputs.
Future AGI Evals: Essential for production reliability with deep evaluation capabilities like hallucination detection, RAG quality analysis. The platform supports custom metrics and automated evaluation that runs 10x faster than manual QA teams, making it suitable for high-stakes production environments.
Step 4: Optimize & Iterate with a Feedback Loop
Use insights from the Future AGI dashboard to continuously improve the agent system.
Technical Details:
Performance Tuning: Identify slow API calls or inefficient prompts highlighted by Future AGI's tracing dashboard. For example, if the Research Agent takes 12 seconds to retrieve leads due to redundant database queries, the dashboard will show the exact bottleneck, allowing you to optimize the query logic.
Accuracy Enhancement: Use the Future AGI Experiment Hub to A/B test different LLMs (GPT-5 vs. Claude 3) or prompt strategies across the same dataset. The platform provides side-by-side comparisons of accuracy, latency, and cost to validate which configuration reduces errors and improves overall quality.
Continuous Improvement: Feed evaluation data back into the development cycle to inform fine-tuning efforts. Future AGI's automated feedback loop identifies flawed prompts or tool usage patterns and generates optimization recommendations, which can inform AgentKit's Reinforcement Fine-Tuning (RFT) capabilities on custom reasoning models
Conclusion
The era of building agents that merely work is over. Production AI demands a new standard: agents you can trust to handle real user requests, maintain accuracy under load, and operate safely at enterprise scale. By combining a best-in-class building experience with OpenAI AgentKit and a comprehensive reliability platform with Future AGI, development teams can innovate faster while ensuring their AI systems deliver accurate, secure, and production-ready results.
Future AGI provides the observability, evaluation, and optimization layer that turns black box agents into transparent, reliable systems. Get started with auto-instrumentation in seconds, run deep evaluations across your agent workflows, and monitor production traffic with real-time dashboards and smart alerts. Build agents you can trust
FAQs
