Adaptive and Learning Agents (ALAs) bring computational intelligence to their Cyber Physical host systems to adapt to novel situations encountered in their complex operational environment. They do so by learning from their experience to improve their performance. RTCA DO-178C specifies a stringent certification process for airborne software which represents several challenges when applied to an ALA in regards of functional completeness, functional correctness, testability and adaptability. This research claims that it is possible to certify an Adaptive Learning Unmanned Aerial Vehicle (UAV) Agent designed as per a Cognitive Architecture with current DO-178C certification process when leveraging a qualified tool (DO-330), Model-Based Development and Verification (DO-331) and Formal Methods (DO-333). The research consists in developing, as a case study, an ALA embedded in a UAV aimed at neutralizing rogue UAVs in the vicinity of civil airports and test it in the field. This article is the plan to complete, by end 2022, a dissertation currently in its confirmation phase.