  The Phase-2 Upgrade of the CMS Level-1 Trigger (L1T) will reconstruct
particles using the Particle Flow algorithm, connecting information from the
tracker, muon, and calorimeter detectors, and enabling fine-grained
reconstruction of high level physics objects like jets. We have developed a jet
reconstruction algorithm using a cone centred on an energetic seed from these
Particle Flow candidates. The implementation is designed to find up to 16 jets
in each Xilinx Ultrascale+ FPGA, with a latency of less than 1 {\mu}s, and
event throughput of 6.7 MHz to fit within the L1T system constraints. Pipelined
processing enables reconstruction of jet collections with different cone sizes
for little additional resource cost. The design of the algorithm also provides
a platform for additional computation using the jet constituents, such as jet
tagging using neural networks. We will describe the implementation, its jet
reconstruction performance, computational metrics, and the developments towards
jet tagging.
