Distributed Computing Continuum Systems (DCCS) unify cloud, fog, edge, and Internet of Things (IoT) into a single execution fabric. At scale, heterogeneity and bursty arrivals make it hard to meet per task deadlines. Prior approaches based on learning, optimization, market mechanisms, or class level slicing depend on global state or iterative coordination. Decisions lag arrivals, isolation is scoped to coarse classes rather than individual tasks, and the deadline violation ratio (DVR) rises. We present Task-level Adaptive Computing Slicing (TACS), a fine grained slicing paradigm that delivers task aligned resource governance through autonomous shard management. For each arriving task, TACS executes decentralized scheduling at the shard level to instantiate an ephemeral micro-slice governed by an autonomous shard formed exactly by the task's participants. Within the shard, participants apply closed form rules to make local resource allocation decisions. This design achieves strict task level isolation and precise, scalable matching between supply and demand without global coordination or model retraining. In simulations with 100 to 1000 heterogeneous nodes and a range of loads and heterogeneity levels, TACS maintains DVRs below 1% and provides steadier, higher throughput than advanced baselines. Under adverse conditions, representative baselines exceed 20% DVR and in several cases require retraining when device populations change.