CONCEPT Cited by 1 source
Adaptive reasoning effort¶
Definition¶
Adaptive reasoning effort is the practice of calibrating the depth of an LLM agent's thinking to the complexity of each query, rather than applying a one-size-fits-all reasoning budget. Simple lookups get fast, shallow answers; complex multi-step research gets deep planning, evaluation of intermediate results, and thorough synthesis.
Atlassian Long Horizon applies this within a single architecture: the same reasoning loop handles both extremes by letting the model calibrate its own reasoning investment per turn. (Source: sources/2026-06-18-atlassian-long-horizon-reasoning-engine)
Mechanism (unspecified)¶
The Atlassian post does not disclose whether adaptive reasoning is: - Model-native — e.g. extended thinking tokens / reasoning budget parameters exposed by the LLM provider. - Orchestrator-controlled — e.g. the orchestrator setting different iteration limits or timeout budgets based on a complexity classifier. - Emergent — the model naturally uses fewer iterations for simple tasks and more for complex ones without explicit control.
Why it matters¶
Without adaptive effort, agent systems face a forced trade-off: - Optimise for speed → shallow answers on complex queries. - Optimise for depth → unnecessary latency on simple queries.
Adaptive effort avoids this by routing reasoning budget dynamically.
Seen in¶
- sources/2026-06-18-atlassian-long-horizon-reasoning-engine — Long Horizon uses adaptive reasoning effort; simple lookups respond quickly, multi-step research engages deeper reasoning.