Backbeat: Language Agents Keep Working While Tools Run

Tool-using language agents commonly suspend reasoning until a call returns. Launching calls in parallel removes serialization but still waits for the slowest final result, even when stable partial output already determines the next action. We introduce Backbeat, a model-agnostic execution interface that represents tool calls as typed futures with launch, observe, resolve, and cancel operations. Tools declare which fields are stable before completion; the agent can reason over these observations, finish when its decision predicate is satisfied, and cancel irrelevant tails. We evaluate Backbeat on PendingBench: 144 fresh tasks across six tool families, three model families, and 1,728 controlled trajectories, followed by complete agent sessions, generated concurrent pytest suites, and unmodified public-package tests. Against parallel-final execution, Backbeat reduces mean virtual evidence-ready latency from 35.21 to 21.56 seconds and raises deadline-correct completion from 25.7% to 93.8%, while ordinary accuracy remains 95.1% versus 95.4%. Even an optimistic adaptive final-only replay remains 11.69 seconds slower and 53.5 deadline points lower. The mechanism is visible in the schedule: 104/144 tasks place the deadline inside a stable tail averaging 13.65 seconds. In complete agent sessions at duration scale 1.0, Backbeat saves 14.47 seconds [11.05, 18.53] and wins all 24 paired runs. Through generated suites executed by pytest 9.1.1, it saves 2.24 session seconds [0.08, 4.38] and 5.46 seconds of executed suite service, with every task answer preserved. On 12 upstream SciPy and scikit-learn files, stable target-test reports precede full-suite completion in all 12 pairs, saving 0.50 tool seconds [0.16, 1.06] at zero API cost. The 1,980-trajectory campaign costs $2.56. Pending calls are not dead time: they are a useful test-time reasoning substrate.