Enterprise Functions

Agent outsourcing (agent workforce)

Outsourced agents instead of outsourced people: iOS developer, business analyst, accountant, tester; with a work log.

The problem

The outsourcing model sells person-months: starting takes weeks, knowledge leaves with the person, timesheets do not show what was done. Scaling means new hires.

What the agents do
  1. 01The job description becomes an agent spec: tasks, tools, outputs, KPIs, approval points.
  2. 02The agent gets accounts in your tools (Jira, Git, ERP, CRM); code and data stay in your tenant.
  3. 03Every output is reviewed in the shadow period; after the quality threshold, sampled review and threshold-based approval.
  4. 04Every task goes to the work log: output, hour-equivalent, quality, approval, cost; weekly timesheet and monthly invoice from the same record.
Outcomes
  • Roles that start within days and work 24/7
  • What was done, how long it took and what it cost in one record
  • Knowledge stays in the workspace; scaling takes minutes
Capabilities used
Role specsWork logShadow → sampled reviewHuman-in-the-loopKill-switchAgent-month / output billing
Explore the platform →
Related agents

This solution in the agent library

Software DeliveryHITL

iOS feature implementation (hired developer agent)

The Jira story is taken, built in SwiftUI, tested and opened as a PR; review and release decisions stay with the team.

380 runs / month 94.4% success 4 agents
Software DeliveryHITL

Regression test cycle (hired QA agent)

Regression runs on every release candidate, new bugs are filed with evidence, a coverage report is written; the “go” decision stays with the release manager.

260 runs / month 95.8% success 4 agents
Enterprise FunctionsHITL

Month-end close and reconciliation (hired accountant agent)

Invoices are processed, bank and vendor reconciliations done, the close checklist run; entries are posted with the accountant’s approval.

60 runs / month 96.2% success 4 agents
Software DeliveryHITL

Backlog grooming and estimation

Incoming requests are triaged, clarified and sized; a sprint proposal goes to the product owner for approval.

340 runs / month 92.8% success 4 agents

Time to move from experimenting with AI to transforming with it.

In a 30-minute discovery session we take your 2–3 priority business problems, show a live demo of a similar scenario, and draft a roadmap that starts with the Value layer.

Book a 30-minute discovery session → dahi@aginies.com
  1. Your 2–3 priority problems
  2. Live demo of a similar scenario
  3. Roadmap starting with value analysis