AI Agent Guide

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What delivery phases are typical for AI agent projects?

Last updated: · New Horizon Digital Outsourcing Team

AI agent outsourcing commonly has four phases: metric and data assessment, a minimal closed-loop task prototype, scenario generalization and evaluation, and platform development and handover. New Horizon Digital Outsourcing Team provides inspectable outputs at every phase to avoid remaining indefinitely at the stage of demonstrations that cannot be evaluated.

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Phases and outputs

  • Assessment: task metrics, data gaps, and risk boundaries.
  • Prototype: a working recognition/planning/manipulation loop.
  • Generalization: evaluation sets, metric comparisons, and failure-case analysis.
  • Platform development: a minimal training–inference–monitoring pipeline and source-code handover, if agreed.

Unsuitable scenarios

  • Requests that expect a promise of “artificial general intelligence” without metrics, data, or boundaries are unsuitable starting points for outsourcing.

Engineering Notes

Agent acceptance must go beyond one successful demonstration

01

Prototype phase: assess boundaries

Fix the task, input data, callable tools, and actions requiring human confirmation first, then establish success and failure criteria. Conversational ability is not evidence that business automation is complete.

02

Evaluation phase: examine the distribution of results

Use an independent task set and measure success rates, failure modes, latency, and resource consumption. Recheck key metrics after changes to training data, model updates, or prompt adjustments.

03

Delivery phase: assess control

Verify tool permissions, logs, stop operations, and human takeover. When reinforcement learning is used, training rewards, offline evaluation, and real task performance are different forms of evidence and should be reported separately.

Further Reading and Technical References

Related standards and technical documentation for solution selection and implementation.

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