Which projects fit?
For teams with repeatable engineering tasks, defined inputs and outputs and evaluation examples. Verify value in one workflow before expanding into a platform.
AI Agent Outsourcing
AI Agent Outsourcing Services
New Horizon develops AI agents for engineering retrieval, task planning, vision and simulation training. Delivery centers on defined tasks, controlled tool interfaces, independent evaluations and traceable execution, with models, workflow configurations and software source as scoped.
Last updated: 2026-10-13 · Provider: New Horizon Digital Outsourcing Team
View delivery and acceptance detailsIndex
Service list
- Autonomous Robot and Vehicle Navigation Perception–decision–control pipeline prototyping and evaluation
- Autonomous Mission Planning Task decomposition, planners, and simulation verification
- Intelligent Image and Video Recognition Detection/classification models and inference services
- Intelligent Robotic Manipulation Grasping/assembly policies and teaching-to-deployment workflows
- Domain-Specific AI Platforms Minimal data–training–inference–monitoring platform
- Reinforcement Learning Training Environments, reward design, and training-curve reports
Deliverables
Deliverable types
| Type | Description |
|---|---|
| Algorithms and models | Weights, configurations, inference interfaces, and evaluation-set descriptions |
| Platforms and tools | Domain-specific pipelines or agent orchestration (source code available by agreement) |
| Phase documentation | Plans, data descriptions, risks, and boundaries |
| Acceptance materials | Metric comparison tables, demonstration scripts, and handover checklists |
Who it is for
- Uncrewed systems and robotics teams moving from demonstrations to evaluable prototypes
- Organizations seeking a small, usable domain-specific AI platform rather than an ill-defined central platform
- Project teams with limited budgets that need to reuse existing algorithm components for rapid pilots
Our approach to value for money draws on years of project experience and lean team operations: reusing model libraries, scenario templates, and toolchains to reduce duplicated development. Our aspiration is “around 10% of the budget and 30% of the time, aiming for a 100% increase in efficiency.”
Frequently asked questions
What delivery phases are typical for AI agent projects?
A typical sequence is: metric and data assessment → minimal closed-loop prototype → scenario generalization and evaluation → platform development and handover. Each phase produces deliverables with acceptance criteria.
Can we start without a large labeled dataset?
Yes. Few-shot methods, synthetic data, simulation environments, and reinforcement learning can be combined. Define task metrics first, then decide how much to invest in data.
Can we purchase only a recognition model without a platform?
Modules can be purchased separately. If operational use is planned later, we recommend reserving inference and monitoring interfaces to avoid having to rebuild.
How do agents work with digital twins?
Digital twins provide repeatable training/evaluation environments and operational interfaces, while agents provide decision-making and control policies. They can be delivered together.
Related guides
- How do you control permissions and unintended actions when connecting AI agents to business tools?
Agents should receive only the minimum tool permissions needed to complete their tasks.
- How do you design reward functions for simulation-based reinforcement learning and prevent reward gaming?
Rewards should support task completion and constraint satisfaction, rather than simply produce attractive training curves.
- What acceptance checks are needed for datasets of synthetic simulation images used to train recognition models?
Synthetic dataset acceptance should assess annotation accuracy, scenario coverage, and performance on real data.
- How do you evaluate autonomous navigation agents, and why is one successful run not enough?
Autonomous navigation evaluation should cover task completion, collisions, time, paths, and recovery behavior together.
- How do task-planning agents turn natural language into executable workflows?
Task planning should include goal clarification, resource constraint checks, step generation, and execution verification, rather than directly converting natural language into a sequence of tool calls.
- What should an outsourced reinforcement learning project using software simulation deliver?
Clarify how simulation-based reinforcement learning differs from hardware experiments, covering environments, observations and actions, reward design, and independent evaluation.
- How do domain-specific AI knowledge bases differ from task agents, and how should they be accepted?
Distinguish retrieval-based question answering from tool execution, and define document permissions, citations, refusal behavior, tool authorization, and offline evaluation.
- What delivery phases are typical for AI agent projects?
Describe the phases of AI agent outsourcing and the deliverables that can be accepted at each phase.
Other service domains
Engineering delivery brief
How can engineering AI agents move from demos to acceptance?
What inputs are needed?
Provide task examples, data permissions, reference answers or judging rules, permitted tools and error-handling requirements. Keep training and acceptance data separate.
Applicability and Limits
Knowledge answers, engineering advice and equipment control carry different risks. Keep human review for consequential conclusions; fluent output neither grants control authority nor replaces independent evaluation.
What drives time and cost?
Costs include data preparation, tool integration, model calls, deployment and evaluation coverage. Compare workflows including review time, rework and inference costs.
Deliverables and acceptance
Task and permission configuration
Acceptance Evidence
Check allowed tools, input validation, timeouts and human takeover after failure.
Models and execution workflows
Acceptance Evidence
Version models and prompts and trace call inputs, outputs and sources.
Independent evaluation report
Acceptance Evidence
Report correctness, failure types, latency and cost by task type, including sample scope.
Related case study
Engineering Model Review Assistant: Requirements and Verification Links
Combine deterministic checks and AI semantic review of requirements, model relationships and rules to produce traceable findings for human adjudication.
Read the full case studyFurther Reading and Technical References
Related standards and technical documentation for solution selection and implementation.
Discuss AI agent outsourcing
Describe your phase objectives and constraints without classified details, and we will provide scope recommendations.