The Game-Changing Release
On March 15, 2026, OpenAI released the Agents SDK, an open-source framework for building multi-step autonomous agents, replacing the Assistants API. The release is not an incremental update: it is a structural repositioning of the operational control point within organizations. The platform, born as a series of experimental patterns, is now formalized into a set of minimal primitives — agents, handoffs, guardrails — that allow the composition of complex flows without reliance on proprietary solutions. The most significant data point is not the technology itself, but its immediate impact: in 5 hours, the project reached 9,900 stars on GitHub, surpassing the growth of LangChain Deep Agents, which had 9,900 stars in 5 hours. This indicates a rapid convergence of technical practices into a single standard. Consequently, the architecture of operational control is no longer a matter of software, but of process. The event is not a news item: it is a symptom of a structural transition.
The transition is also evident in market data. Allica Bank recorded a 23% growth in its loan book and a 27% increase in revenue in 2025, partly attributable to the adoption of AI agents in the credit assessment process. The ability to automate the document analysis cycle, verify documents, and assess risk reduced the average processing time from 72 hours to 14 hours. The system does not replace the human, but replaces them in a critical point: the processing time. This implies that the strategic value is no longer in the model, but in the flow that feeds it. The data of 55,000 technology layoffs in 2025 is not an isolated event: it is the cost of transitioning from a work model based on repetitive tasks to one based on the supervision of agents. The surprising aspect is not automation, but its standardization.
Autonomous Control Architecture
The OpenAI Agents SDK operates on a fine-grained orchestration paradigm, where each agent is a node in a decision network. The framework does not provide predefined agents, but primitives: an agent can be sent to a task, return results, and pass control to another agent without human intervention. This model is similar to a workflow system, but with the ability to self-regulate. Each agent can be configured with guardrails — security rules — that limit access to sensitive resources. The control mechanism is not based on API keys, but on detached permissions: an open protocol, Grantex, allows assigning specific rights to each agent, with fine-grained revocation and audit trail. This is not an improvement in security: it is a paradigm shift. Security is no longer a property of the system, but a property of the flow.
The technical surprise is the ability to interact with the physical environment. The Computer Use Tool, included in the framework, allows the agent to perform actions on a computer — open files, edit documents, execute scripts. This transforms the agent from an analysis tool to a physical executor. The effect is not an increase in productivity, but a reduction in the gap between intention and action. The latency time between a decision and its implementation goes from hours to seconds. In a financing context, this means that an agent can verify a contract, compare it to a risk model, and sign it automatically, with a complete trace. The operational consequence is that the decision-making process is no longer linear, but iterative and retroactive. The agent not only decides, but learns from the result of its action. The data of 9020 mAh battery of the Vivo T5 Pro 5G is not a marketing detail: it is an example of how hardware is now designed to support the continuous operation of agents, even in remote environments.
The Imperfect Symbiosis
The market reacts with a combination of enthusiasm and inadequacy. While funds are flowing into platforms like SolvaPay, which builds payment infrastructures for agentic commerce, and Brix, which tokenizes emerging assets, the operational reality is more complex. The autonomous agent cannot function without a continuous flow of data, a robust security infrastructure, and clear governance. The quote from Gary Marcus is revealing: “Mythos is more sophisticated but perhaps not head-and-shoulders the way it was portrayed.” The neurosymbolic AI represents an advancement, but does not solve the problem of semantic understanding in unstructured contexts. The agent can perform a task, but does not understand the context. The risk is not that the agent makes a mistake, but that it acts correctly on an incorrect input.
“Claude Mythos fears… drama and bs” — Yann LeCun
The tension between technology and perception is evident. Concerns about the effectiveness of Claude Mythos are often exaggerated, but not irrelevant. The agent is not an autonomous entity: it is a complex system of interactions between models, data, and rules. The risk is not the agent itself, but its integration into unprepared processes. The growth of 3 million gig workers in Nigeria is not a sign of freedom: it is a sign of a lack of formal infrastructure. The agent can be the last tool of a system that already functions poorly. In this sense, the symbiosis is imperfect: the agent is more efficient, but not more resilient. The data of 17.5 million gig workers in East Africa is an indicator of structural vulnerability, not of innovation.
Scenarios and Conclusion
By the next election cycle, the operational control of companies will be dominated by autonomous agents. The euphoria that speaks of revolution ignores the fact that the transition is constrained by two factors: the thermodynamic efficiency of the data flow and the buffering capacity of the infrastructures. The catastrophism that predicts the collapse of the labor market ignores the fact that work does not disappear, but transforms. The data of 55,000 layoffs is not a signal of the end of human work, but of a transition to a role of supervision and verification. The surprising aspect is that the value is no longer in the task, but in the control of the process. The system is no longer a chain of command, but a network of agents that self-regulate.
The emerging constraint is the dependence on the flow of energy. Data centers must now show energy bills: a data point that is not only regulatory, but strategic. The efficiency of an agent is not only in terms of output, but in terms of input. The next bottleneck will not be computing power, but the availability of water for passive cooling. Calyos has demonstrated that passive cooling is possible in Europe, but it requires investment in physical infrastructure. The flow of energy is the new logistics node. The next move is not to build more intelligent agents, but to build more efficient systems. Operational control is no longer a matter of software: it is a matter of material flow. My assessment is that the architecture of autonomous control is already in place, but its sustainability will depend on the input-output balance of the physical system.
📷 Photo by Zac Wolff on Unsplash
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