# Enterprise AI Agents — Alector Lab Engineering Specification

Canonical URL: https://alectorlab.com/services/enterprise-ai-agents
Raw Markdown: https://alectorlab.com/services/enterprise-ai-agents/raw
Last Updated: 2026-11-02
Provider: Alector Lab – Enabling Technologies Ltd (https://alectorlab.com)
Contact: alectorlab@gmail.com

## Summary
Bounded agent systems that can reason across context and take controlled action through existing business APIs.

## Engineering Overview
Use an agent when a workflow genuinely needs contextual decisions or tool selection. Keep fixed rules in ordinary software. An engagement defines what the agent may read, propose and execute, together with how a person reviews uncertain or consequential actions.

### Illustrative Scope
A service-intake assistant could classify a request, retrieve the customer record and propose a dispatch task. A coordinator approves the booking before a write occurs. Missing customer matches, stale availability and tool failures go to a review queue rather than becoming invented confirmations.

## Target Audience
- Operations teams with multi-step knowledge work
- SaaS products adding action-oriented assistants
- Enterprises requiring human approval and auditability

## Supported Workflows
- Knowledge Desk and source-linked answers
- Document Operations and reviewed extraction
- Proposal Studio and approved drafting
- Support Triage and response preparation
- Operations Analyst and traceable reports
- Meeting-to-Action and reviewed task lists

## Required Inputs
- Workflow states and policies
- API and system access
- Examples of normal and failure cases
- Escalation and approval rules

## Engineering Deliverables & Outputs
- Agent application and execution graph
- Typed tool contracts and agreed integrations
- Human-review queues with illustrative workflow examples
- Fully local or cloud deployment package
- Evaluation, traces, audit logs and operating handover

## Delivery Stages
1. Map deterministic and probabilistic steps
2. Prototype on historical tasks
3. Define permission boundaries
4. Stage actions from suggestion to execution
5. Monitor quality, cost and drift

## Acceptance Criteria
Evaluate successful task completion alongside unauthorized-action attempts, duplicate actions and escalation quality. Deliverables include tool contracts and retry behavior. Access to source systems is scoped to the minimum necessary permissions; autonomous authority is never implied by an agent demo.

## Operating Boundaries
- **Integrations:** Start with approved files and local exports for offline builds, or scope permitted CRM, knowledge, ticketing and operational connectors for cloud builds. Separate read tools from writes; agree provider-enforced idempotency and timeout reconciliation. Specific connectors are agreed during scoping.
- **Deployment:** Choose fully local inference, parsing, retrieval, storage and tools with no required cloud calls after setup, or an agreed cloud environment with approved providers and data-handling terms. Size local hardware against representative workloads and include maintenance, backups and updates. Quote cloud hosting, inference, monitoring and support separately. Start in suggestion mode; network access, credentials and tenant isolation belong to the application boundary, not the prompt.
- **Human Review & Governance:** A named operator authorizes consequential actions and resolves uncertain matches. Define a stop control and compensating actions for effects that cannot be rolled back.

## Operating Limitations & Safety Boundaries
- Agents should not replace deterministic rules unnecessarily
- External actions require idempotency and approval design
- Quality depends on source systems and evaluation coverage

## Common Technical Question
**Q: Does the agent need permission to change our systems?**
A: Not initially. A read-only or suggestion-only pilot can establish usefulness. Write access is a separate rollout decision with explicit approval rules, rollback or compensation behavior and monitoring.