Use Cases

AI Agents in Production

Six concrete patterns for orchestrating autonomous AI agents in enterprise production — across AdTech, logistics, finance, customer support, HR, and e-commerce.

Advertising Technology

AdTech Yield Optimization

AdTech publishers need to optimise ad yield in real time across thousands of placements. LeafMesh orchestrates a crew of agents — one analyzes performance, one proposes changes, one applies policy, one executes — with a person weighing in on anything above the threshold. Used by leading SSPs and DSPs.

Agent Pattern

4-agent crew: analyzer → optimizer → policy → executor

Governance

Ad-spend changes above $10,000 get a person's judgment; every change logged

Outcome

22% yield uplift; full audit trail per change

Logistics

Logistics & Supply Chain Coordination

Logistics ops teams use LeafMesh to coordinate AI agents across routing, exception handling, and vendor switching. When a shipment risks SLA breach, agents reroute automatically; novel breaches escalate to humans with full context. The shared memory means every agent sees the same view of the world.

Agent Pattern

Routing agent + exception agent + vendor swap agent

Governance

Auto-approve under SLA; escalate breaches

Outcome

3 reroutes/day average; zero SLA breaches

Finance

Finance Operations Automation

Enterprise finance teams use LeafMesh to triage and process invoices. An autonomous AI agent reviews each invoice; under policy thresholds, it clears automatically; above thresholds, it routes to a person for judgment, with the policy reference attached. Every decision is auditable for SOX, GDPR, or DPDP compliance.

Agent Pattern

Invoice triage agent + policy-bound auto-approval

Governance

Every decision logged with policy reference

Outcome

80% of invoices auto-processed within compliance bounds

Customer Support

Customer Support Agent Orchestration

Customer support teams orchestrate a tier-1 AI agent (mix of GPT-4 and Claude via capability routing) to handle common requests. When confidence drops below threshold, LeafMesh hands off to a human with the entire conversation, customer history, and the agent's reasoning attached. No re-asking the customer.

Agent Pattern

Tier-1 agent → confidence-routed handoff to human

Governance

Confidence threshold drives escalation

Outcome

65% deflection rate; humans get full context on handoff

HR / Talent

Talent Acquisition with Bias Governance

HR teams use LeafMesh to screen candidates while maintaining auditable fairness. A screening agent evaluates resumes; a separate bias-check governance layer reviews each decision against fairness criteria. Bias alerts trigger human review. Quarterly fairness reports are generated automatically from the audit trail.

Agent Pattern

Screening agent + bias-check governance layer

Governance

Every screening decision auditable; bias alerts trigger review

Outcome

Faster screening, audited fairness reports

E-commerce

E-commerce Operations

E-commerce ops teams coordinate pricing, inventory, and promotion decisions through a multi-agent system. Without coordination, pricing might mark down a product the inventory agent is trying to clear at full price. With LeafMesh shared memory, every agent sees the others' state, and policy prevents conflicting decisions.

Agent Pattern

Pricing + inventory + promotion agents share state

Governance

Cross-agent policy: no conflicting pricing decisions

Outcome

Real-time coordinated decisions across siloed systems

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