NexG service
AI agents.
NexG designs AI agents around a defined business decision or operational handoff, not around a demo prompt. The work starts by mapping data, tools, permissions, failure modes, and human review, then turns that map into an agent that can be evaluated, observed, and operated as part of a real workflow.
When it fits
Start with the operating need.
An internal copilot needs permission-aware access to company knowledge and tools.
A support or operations workflow needs draft generation, triage, or structured handoffs without removing human accountability.
An existing agent prototype works in a demo but lacks evaluation, observability, or safe recovery paths.
Delivery shape
Useful output with a clear handover.
Deliverables
What the team receives
- Workflow, decision-boundary, and exception map
- Tool and data-access design with explicit permission boundaries
- Agent implementation, prompts, structured outputs, and integration adapters
- Representative evaluation cases and acceptance criteria
- Human approval, escalation, retry, and stop controls
- Operational telemetry, runbook, and handover documentation
Controls
How delivery risk is handled
- Structured outputs and schema validation at system boundaries
- Human approval before consequential or irreversible actions
- Evaluation sets that cover normal, ambiguous, adversarial, and failure cases
- Traceable tool calls, model responses, costs, and exceptions
- Fallback and stop paths when confidence, data quality, or dependencies are inadequate
Technology
Tools follow the constraints
- Large-language-model and embedding APIs selected for the use case
- Tool calling and Model Context Protocol integrations
- Retrieval, search, and vector-capable data stores
- Queues, workflow orchestration, and scheduled jobs
- Evaluation harnesses and structured test datasets
- Application logs, traces, and operational dashboards
Delivery process
What happens once scope is agreed.
Each stage resolves a different class of uncertainty while keeping decisions visible to the people responsible for the system.
- 01
Frame the decision and context
Define what the agent may decide, what it may only recommend, and where a person remains accountable, then identify the authoritative data, tool permissions, latency constraints, and systems affected by each action.
- 02
Prototype against real cases
Build the narrowest useful flow and test it against representative normal, ambiguous, and failure cases.
- 03
Engineer the operating path
Add integrations, state handling, approvals, recovery behavior, logging, and deployable infrastructure.
- 04
Evaluate and evolve
Review behavior against agreed cases and use production feedback to improve prompts, tools, and boundaries deliberately.
Intended outcomes
A stronger production path.
These intended outcomes describe the system, controls, and ownership the engagement is designed to leave behind.
Outcome 01
A bounded automation with explicit authority and human handoffs
Outcome 02
Repeatable evidence for reviewing agent behavior before and after release
Outcome 03
A workflow that can fail visibly and recover safely
Outcome 04
Documentation that lets the operating team understand and change the system
Evidence you can inspect
Follow the work behind the thinking.
These are published NexG products, public repositories, or field notes related to this capability. They are provided for inspection, not presented as customer outcome claims.
Published 01
nex-skills
Public, reusable workflow material for agent-oriented engineering practices.
Inspect the sourcePublished 02
NexMemory MCP
An inspectable Model Context Protocol integration for memory-oriented workflows.
Inspect the sourcePublished 03
Evaluating AI agents with guardrails
A field guide to cases, boundaries, review signals, and production controls.
Inspect the sourceQuestions
What buyers usually ask.
A first conversation can clarify fit, ownership, and the information needed before a proposal.
Good candidates have a clear trigger, accessible context, a reviewable output, and an owner for exceptions. A workflow with unclear accountability or no way to judge a correct result should be clarified before it is automated.
Only where the action is low risk and that boundary is agreed explicitly. Consequential actions can be limited to recommendations, drafts, or approval-gated tool calls, with escalation when the available evidence is insufficient.
No. Model choice follows the workflow's quality, privacy, latency, integration, and cost constraints. The architecture should keep avoidable provider coupling out of the business logic.
Yes. The first step is to inspect its current behavior, dependencies, data access, and failure modes, then decide what can be retained and what needs production controls.