As one of 20 projects picked from nearly 600 applicants to Germany's federal Agentic AI Hub, the Borken district used an AI agent to pre-review 100-page long-term-care-assistance applications, cutting selected work steps by 35% and total processing effort by about a fifth.
35 %
Reduction in time on selected work steps (3-month pilot, Mar–May 2026)
20 %
Reduction in total application-processing effort (3-month pilot)
15 of 18 evaluable cases
Cases where staff felt 'secure' relying on tool output (3-month pilot)
70 %
Programme-wide piloted solutions reaching operational use (2026 Agentic AI Hub cohort (20 projects))
95 %
Participating municipalities highly satisfied (2026 Agentic AI Hub cohort)
Details
Maturity
Pilot
Promoter
Kreis Borken (Sozialamt) with deepset
Period
March–May 2026 (3-month pilot)
Region (NUTS)
DEA34
Keywords
social services, agentic AI, welfare administration, document processing, local government
Context
Kreis Borken, a district administration in North Rhine-Westphalia, was selected as one of 20 pilot projects (from nearly 600 applications nationwide) under the German Federal Ministry for Digital and State Modernisation's (BMDS) 'Agentic AI Hub' programme, which paired 19 municipalities with 9 AI start-ups between March and May 2026.
Objectives
The pilot aimed to test whether an autonomous AI agent could pre-review 100-plus-page applications for 'Hilfe zur Pflege' (state long-term-care assistance), checking completeness and preparing financial data for means-testing while keeping a human reviewer in the loop.
Activities
Working with Berlin-based AI start-up deepset on its Haystack framework, Kreis Borken's Social Services department deployed the agent to check submissions for completeness, extract and prepare financial data, and draft a recommendation for the caseworker's decision.
Results
Over the three-month pilot, Kreis Borken measured a 35% reduction in time spent on selected work steps and roughly a 20% reduction across the whole application process; in 15 of 18 evaluable cases, staff reported feeling 'secure' relying on the tool's output. Programme-wide, the BMDS reported that 70% of the 20 piloted solutions reached operational use, 95% of participating municipalities were highly satisfied, and half were assessed as directly transferable to other municipalities.
Conclusions
Results come from a short, small-sample pilot self-reported by the district and sponsoring ministry, without independent external audit; the federal programme's next step is to build a legally compliant procurement pathway before any wider rollout.
Implementation
Indicative cost
Low (< €50k) — Not publicly disclosed; a single-district, 3-month pilot pairing one municipality with one AI start-up under a federally funded programme.
Time to results
Short (< 1 year) — 3-month pilot, March–May 2026, within the federal Agentic AI Hub programme; wider rollout pending a procurement framework.
Staffing & skills
Kreis Borken Social Services department (Sozialamt) caseworkers, with a human reviewer kept in the loop, Berlin-based AI start-up deepset, providing the Haystack framework and technical build, German Federal Ministry for Digital and State Modernisation (BMDS) as programme sponsor and evaluator
Conditions for success
Pairing a municipality with a specialised AI start-up through a competitive federal selection process (19 municipalities x 9 start-ups)
Keeping a human caseworker in the loop to review the agent's recommendation rather than fully automating decisions
A defined, monitored evaluation sample (18 evaluable cases) to measure before/after time savings
Common failure modes
Small sample size (18 evaluable cases) over a short 3-month window limits generalisability
Self-reported results without independent external audit
National scaling depends on a legally compliant procurement framework the ministry says it is still building