Opus 4.5 is not the normal AI agent experience that I have had thus far

Implements multi-step reasoning chains where the model explicitly works through problems step-by-step, refining intermediate conclusions before producing final outputs. Uses internal chain-of-thought patterns to decompose complex tasks into substeps, with each step building on previous reasoning rather than jumping directly to answers. This approach surfaces reasoning artifacts that developers can inspect, validate, and guide toward better solutions.

Automatically breaks down complex user requests into discrete subtasks with adaptive sequencing based on dependencies and available tools. The model constructs execution plans that can be modified mid-execution based on intermediate results, rather than following a rigid predetermined sequence. This enables agents to handle ambiguous requirements, discover new subtasks based on partial results, and recover from failed steps by replanning.

Enables agents to select and invoke tools based on dynamic capability assessment rather than static tool definitions. The model evaluates what tools are available, what each can accomplish, and whether they're appropriate for the current task context — including assessing tool limitations and potential failure modes before invocation. This goes beyond simple function calling by adding a negotiation layer where the agent can reason about tool fitness and suggest alternatives if primary tools are unsuitable.

Processes and reasons over very large context windows (potentially entire codebases, documentation sets, or conversation histories) while maintaining coherent reasoning about relationships and dependencies across the full context. Uses architectural patterns that allow the model to reference and reason about distant context elements without losing track of earlier information. This enables agents to make decisions based on holistic understanding rather than summarized or windowed context.

Supports workflows where agents produce intermediate outputs that humans can inspect, critique, and guide before the agent proceeds to refinement. The agent can accept structured feedback (e.g., 'this approach is wrong because...', 'focus on X instead of Y') and incorporate it into its reasoning for the next iteration. This creates a collaborative loop where human judgment guides agent reasoning without requiring full manual intervention.