OECD-DAC GovNet
Reforming the development sector for agentic AI (and whatever comes next)
Sinanoglu, SemuhiThe Current Column (2026)
Bonn: German Institute of Development and Sustainability (IDOS), The Current Column of 7 May 2026
Bonn, 7 May 2026. Agentic AI is arriving in development cooperation before the sector can govern it. Germany should play a leadership role in the sector’s technological transformation.
Agentic AI is arriving in development cooperation before the sector has the means to govern it. Unlike the chatbots most practitioners have used, agentic systems take actions and coordinate on their own by calling other tools and stringing together steps to complete a task. When the OECD DAC Governance Network meets this month to discuss AI, the hard question to answer is how to adopt and govern such a rapidly evolving and increasingly autonomous technology. As the top donor among DAC countries, Germany should play a leadership role in the development sector’s technological transformation. The Federal Ministry for Economic Cooperation and Development (BMZ) can host a pooled technical capacity that experiments with AI tools and sets accountability standards. What it needs is policy willingness and an institutional framework that treats AI as a moving technological frontier.
The development cooperation sector is not ready for agentic AI
The pace of change is the first problem. In only eighteen months, the technological frontier has moved substantively from chatbots that retrieve information to systems that can generate data and act across multiple tools without supervision. Stanford’s AI Index 2025 documents that the cost of running these models has also fallen more than 280-fold over the same period, with lowered barriers to advanced AI. With innovation cycles measured in weeks, the development cooperation sector is at odds with this pace, with long procurement timelines and rigid bureaucracy.
This mismatch creates a capacity problem. Development organisations are struggling to simultaneously track the AI innovations and build tools for their programming, while closing internal skills gaps. The likely outcome would be individual staff experimenting on the side, along with small in-house units using whichever commercial model is cheapest.
There are two implications of this capacity constraint. First, it will impede scalability. An OECD overview of existing AI use cases suggests that most pilots stay in the exploratory phase and rarely scale. Second, haphazard AI applications will lead to fragmentation with incompatible standards and create an accountability gap across the sector.
The deeper constraint is data. AI is only as capable as the information environment it draws on. In fragile states and digitally under-connected communities, where, for example, many under-resourced African languages barely appear in training data, agentic systems will produce misleading outputs, with grave implications for development programming.
BMZ’s leadership role
BMZ has done some groundwork that can be leveraged for sectoral reform. The BMZ Data Lab has developed pipelines and a ministerial chatbot (KIEZ). FAIR Forward has worked with seven partner countries on open AI data and policy, and produced insightful resources. The BMZ’s new reform plan also cites AI in three places, including the establishment of a new working hub for strategic foresight. But the plan still treats AI as an instrument to be deployed, not as a moving frontier that needs continuous institutional capacity to track.
BMZ could establish a team that serves as a sector-wide AI capability. Consider an agile, fast-moving unit of engineers and development practitioners working to pool resources across different stakeholders, develop protocols, and stress-test cutting-edge AI tools for donors and development organisations for safe yet faster adoption. Such an AI team would have a broader mandate than just serving the ministry internally. The team would work on shorter contracting and procurement cycles than the sector typically allows, with clauses that let tools evolve as the technology does. Smaller donors and other development organisations would tap the same resource through co-development, joint training, and open access to the tools and protocols it produces.
This kind of unit answers two problems at once. BMZ would be a central node for pooling resources together for joint capacity within the sector. And it can generate sector-specific algorithmic accountability standards, for example, mandatory decision-audit trails for any agentic AI system. These tools, in turn, will strengthen the BMZ to actively shape the normative discussions for the use of AI in development cooperation globally.
The same unit can also tackle the data problem. It could create guidelines for “data readiness assessments”, as a standard prerequisite for any AI-assisted programming (akin to environmental impact assessments). It can also help turn decades of tacit institutional knowledge within the sector into publicly available, AI-assisted, and actionable knowledge (going beyond KIEZ) that could, first, give more visibility to digitally under-connected communities, and second, empower practitioners globally. Instead of a 100-page static PDF toolkit, imagine an interactive system that a practitioner can ask: “What worked in contexts similar to mine?” and receive synthesised, sourced answers.
Germany should not miss this narrow window of opportunity to be the leading voice in AI transformation for development cooperation.