Reflections on OpenAI DevDay 2025: building beyond the POC plateau

OpenAI’s DevDay 2025 delivered a flurry of headline-grabbing launches: from new ChatGPT apps and AgentKit for building agents, to the next-gen Codex, smarter and cheaper GPT image and voice models, and the continued push into embodied intelligence with Sora 2 for video generation. We also witnessed a first-of-its-kind token utilization-based developer productivity leaderboard, while more traditional leaderboards are based on code commits, code reviews and pull-requests (PR) popular among developer communities such as GitHub. The sheer velocity of API enhancements, model turbocharging, and integrated developer tools is nothing short of spectacular—OpenAI remains deeply committed to abstracting away the barriers between idea and software. But for anyone experienced in deploying AI outside the hackathon–Proof-of-Concept (POC) pipeline, the underlying challenge persists: these tools dazzle in demos, but the real hard work of generative AI (GenAI) productization still depends on skilled people doing nuanced, hard work — it’s not yet fully automated or "easy." That’s not a flaw; it’s a reflection of how quickly OpenAI is advancing the frontier, leaving space for the ecosystem to build the operational scaffolding required for real-world deployment.

The promise and the pitfalls of OpenAI’s new tooling

OpenAI’s latest offerings undoubtedly lower the threshold for experimentation even further: AgentKit makes agent orchestration feel like plug-and-play, Codex’s SDK and Zillow or Slack integrations make coding agents and copilots almost trivial, and the new mini models let teams quickly test multi-modal applications on a budget. For POCs and prototypes, these are powerful enablers—any developer can now spin up an agentic system armed with voice, vision, and code in minutes to hours. Sam Altman and team surely deliver towards his vision of “the One-Person Billion-Dollar Company.”

But as anyone who’s tried to scale these systems knows, there’s a tectonic shift between working demo and robust, production-grade solution. The “last mile” of agentic AI—wiring up real business data, negotiating legacy APIs, implementing granular security controls, and deeply aligning autonomous actions with business rules—cannot be solved by an SDK alone. Each organization’s data, compliance constraints, and operational specifics demand custom engineering and, most importantly, deep domain expertise.

Agentic AI needs more than an SDK — it needs systems thinking

That reality is reinforced by the viral August 2025 MIT Tech Review report, which found that 95% of GenAI pilots fail to reach meaningful adoption, with a key root cause being companies’ reluctance to confront “friction.” Teams deploy AI copilots with generic models, hope for magic, and avoid grappling with the annoying details—wrestling with APIs, structuring data, managing access permissions, and capturing the granular business know-how that LLMs simply don’t have. The hype cycle tempts leadership to idolize off-the-shelf AI know-how, while successful use requires a gritty, bottom-up process of embedding real institutional knowledge into the agent’s reasoning frameworks. The opportunity ahead is to marry these powerful tools with grounded, domain-specific context. This is where enterprise builders can play a leading role.

OpenAI’s tools represent a meaningful leap forward, making agent development more accessible than ever. But building agentic systems that reliably interact with company knowledge, handle sensitive data, or automate business workflows requires an intimate understanding of both the problem domain and the AI limitations. Out-of-the-box LLM knowledge and even advanced retrieval-augmented generation (RAG) pipelines simply don’t “know” enough—they lack live context, organizational nuance, and the complex policy rules required for safe automation.

To move from SDK to system, organizations must tackle a set of complex, interrelated challenges head-on:

• Agent orchestration and context management: Coordinating the right combination of agents, tools, and workflows for dynamic enterprise environments, maintaining long running agent sessions while avoiding overflow or memory leaks.

• Observability and debugging: Instrumenting agent decision-making processes to understand why agents made specific choices in production.

• Human-in-the-loop workflows: Designing escalation paths and approval gates for high-stakes decisions agents shouldn't make autonomously.

• Scalability and token optimization: Scaling systems to support many concurrent agents while optimizing cost and performance.

• Agent safety: Protecting against malicious inputs that attempt to poison the context or extract sensitive prompts.

• Governance and compliance: Establishing policy engines and audit trails so agents act within company rules and regulations. This includes enforcing access controls, logging decisions for traceability, and keeping policies versioned and up to date as standards evolve.

These are not problems solved by generated code snippets or demos. They require system-level thinking and measurable standards. OpenAI has made it easier than ever to build experimental agents; now the opportunity is to build resilient, trustworthy ones. Contrary to the token consumption-based leaderboard, the token-to-resolve optimization would play a critical role. That’s where agentic benchmarking frameworks come in.

The path forward

OpenAI’s latest releases are major steps towards democratizing AI innovation—they unlock new creative powers for the world’s developers and lower the cost of curiosity. Yet, for those tasked with building safe, scalable, and truly valuable solutions, the biggest gap isn’t developer productivity— it’s a deep understanding of the problem and the ability to integrate that understanding into real systems. The future belongs to those willing to do the hard, custom work at the interface of AI, data, systems, and human expertise. If GenAI is to graduate from the POC graveyard, the industry must recognize: agents are not “magic glue”—they are tools that, without careful grounding in domain logic and organizational context, will continue to struggle beyond the demo stage.

Enter “Agentic AI Benchmarking Frameworks.” While HELM-like leaderboards compare models, agentic benchmarks compare a combination of agents + models. Introducing agentic benchmarking frameworks fundamentally improves the chances of scaling agentic AI systems to production. Unlike ad hoc testing, such frameworks offer a rigorous, modular benchmark for evaluating how agents perform across complex, real-world scenarios. One such framework, Terminal-bench (tbench), evaluates agent performances on terminal commands, dynamic databases, and human-like user interactions. It quantifies not just accuracy and reliability, but also adaptability—measuring agents’ ability to follow nuanced domain-specific policies and to consistently deliver successful outcomes over multiple runs yet optimize token utilization. Overall, this framework would result in a leaderboard for agents.

In a landscape where POCs frequently stumble when exposed to messy reality, agentic benchmark frameworks give developers a way to diagnose where their agent stacks up against production standards before deployment, revealing critical failure points in reasoning, data handling, or policy compliance. The ability to simulate true-to-life workflows means teams can focus on custom engineering on areas that matter—closing gaps between demo performance and operational robustness. By embedding such agentic benchmarking frameworks in the dev pipeline, organizations move closer to building agentic AI systems that are not only innovative, but deployable and trustworthy at scale—accelerating the transition from proof-of-concept to real-world impact. Agentic benchmarks such as tbench are in a nascent stage, more to come on how Centific is contributing to building a comprehensive agentic benchmarking ecosystem.