Custom AI agents with tools, guardrails and accountability
We build task-focused AI agents that can retrieve context, use approved business systems and hand uncertain or sensitive decisions to people.
What this means in practice
A reliable agent is a controlled software system around a model. It limits which tools can run, validates inputs and outputs, records what happened and makes uncertainty visible. Multi-agent designs are used only where role separation improves control or performance—not as complexity for its own sake.
Problems this work is designed to solve
- Teams copying data between disconnected systems
- Unstructured inbox, document or support triage
- AI prototypes that hallucinate actions or lack permissions
- Opaque automation with no evaluation or audit trail
Typical delivery scope
Agent and tool architecture
Define agent responsibilities, tool contracts, permissions, state transitions and escalation rules.
Business-system integrations
Connect the agent to approved CRM, ticketing, knowledge, database and messaging functions.
Evaluation harness
Test expected tasks, adversarial cases and failure handling against a versioned evaluation set.
Observability and human review
Capture tool calls, latency, model cost and review outcomes without exposing secrets or unnecessary personal data.
How a project moves from idea to production
Discover
Document the current workflow, baseline cost and failure modes, then agree the outcome and guardrails.
Prove
Build a narrow working slice with real data and evaluate quality, latency, cost and operational fit.
Deploy and improve
Integrate the system, add monitoring and human controls, then improve it against measured production behaviour.
Questions teams ask before starting
Can an AI agent safely write to our CRM?
Yes, if writes are limited to explicit tools with schema validation, least-privilege credentials, idempotency and audit logs. High-impact changes should require confirmation or human approval.
Do we need multiple AI agents?
Usually not at first. A single agent with well-designed tools is easier to evaluate and operate. Multiple agents are justified when specialised responsibilities, permissions or independent review materially improve the workflow.
Which AI model should we use?
The answer depends on task quality, latency, privacy, context size and cost. Neuronex evaluates suitable models against your real tasks and can route different task classes to different providers rather than locking the whole system to one model.