All case studies
Agentic AI

Agentic Workflow Automation

Multi-agent pipelines that replace entire operational workflows

70-90%
Manual work reduced
< 3s
Decision latency
< 10%
Human escalation rate
Kubernetes
Deployment

The problem

Growing businesses hit a wall where manual coordination between tools (CRM, email, spreadsheets, order systems) consumes engineering and operations bandwidth. Off-the-shelf automation tools (Zapier, Make) handle linear flows but break on anything requiring judgment, dynamic branching, or multi-step reasoning over unstructured data. The result is hybrid systems where humans do all the ambiguous steps - which is most of the work.

Our approach

We architect multi-agent systems using a conductor/executor pattern. A planner agent decomposes incoming triggers (email, webhook, API event, scheduled timer) into subtasks and routes them to specialized executor agents, each connected to specific external tools via typed interfaces (email, calendar, database, third-party APIs). Agents operate with defined authority scopes: routine decisions execute autonomously with structured logging; ambiguous or high-stakes decisions surface as human-in-the-loop approval tasks with full context. State is persisted across runs in a structured task database. Every action produces an audit trail. Deployed on Kubernetes with per-agent observability dashboards in Grafana.

Outcome

Deployed in the Vosíme pizza checker pipeline (quality decision routing, automated claims), in internal client reporting workflows, and as the core of the Algovectra Vectra Nexus self-management system. Typical outcome: 70-90% reduction in manual coordination work per workflow deployed, with human escalation rates below 10% in steady state.

Deliverables

  • System architecture design: agent topology, authority boundaries, escalation paths
  • Conductor agent implementation with task decomposition and routing logic
  • Executor agents per tool (email, calendar, database, external API)
  • Human-in-the-loop approval interface with full action context
  • Structured audit log per agent action
  • Grafana observability dashboards per agent
  • Deployment on Kubernetes with horizontal scaling

Timeline

4-8 weeks per workflow

Tech stack

PythonTypeScriptLLM APIsKubernetesGrafanaPrometheusPostgreSQLRedisWebhooks

Tags

Agentic AILLM OrchestrationKubernetesPythonTypeScriptGrafanaWebhooksMulti-agent

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