Agentic Workflow

Evaluating the per-task cost of large models requires considering both the token unit price and the number of tokens consumed to complete the task; the output token consumption of chain-of-thought reasoning models may increase by several times or even dozens of times.

In multi-step agent scenarios, the platform's orchestration capabilities often determine the final outcome more than the quality of a single model inference.

CrashDB-QA is a fully local, database-schema-based agentic workflow designed to analyze crash databases directly through natural language.

The difference between agent workflows and traditional RPA is that agents can handle unstructured input and reason on their own to respond when encountering anomalies, without having to rely on pre-hardcoded fixed rules.

The core difference between agentic workflows and a single API call lies in control flow: the model autonomously decides within a loop which tools to call, in what order to execute them, and when to terminate, and adjusts its next actions based on intermediate results.

Agentic workflows introduce new engineering requirements: permission isolation for tool calls, timeouts and retries for long tasks, compression and summarization of context windows, and observability of execution traces.

When starting from zero, it is recommended to first run a single task through the closed loop of "instruction→execution→acceptance", then string them into a fixed process, and finally introduce multi-agent division of labor.

Knowing how to write prompts is no longer a core advantage; mastering agent workflows is the dividing line.

Capability reinforcement process: role-restricted tools make certain invalid operations unavailable to the agent at specific stages.

For agent workflows that skip interactive approval, safety controls must be in place before the first command is executed.

Handing a process that could have been hard-coded over to the model for decision-making is a common anti-pattern, leading to unstable results on every execution, difficult debugging, and high cost.

For deterministic processes whose step order is always the same, the workflow should be hard-coded directly in code rather than letting the model make choices.

In large-scale, high-frequency agentic workflows, cost efficiency directly determines whether a solution can be scaled and deployed.

Deploying such workflows in production environments requires attention to controlling the maximum number of iterations, the token cost of each round of evaluation, and the maintainability of evaluation criteria.

An agent-based clip selection workflow can adopt an orchestration process of batch generation, per-dimension scoring by vision models, automatic elimination of clips below a threshold, and high-scoring clips entering human review.

Gartner predicts that token costs will fall by 95% by 2030, while agent workflow costs will rise more than 5 times by 2028.

Gartner coined the term "Inference Paradox" in an August 2026 press release and predicted that despite the continued decline in per-token prices, the inference cost per agent workflow will grow more than fivefold by 2028.