We design and deploy autonomous multi-agent systems that handle complex enterprise workflows end-to-end — with human-in-the-loop governance where it matters.
Traditional RPA and scripted automation break the moment a workflow branches. They can't reason, adapt, or handle exceptions — so a human still has to babysit every edge case.
Agentic AI changes this. Instead of brittle scripts, you deploy AI agents that understand goals, plan multi-step actions, call tools, and escalate to humans only when the stakes require it.
RPA handles predictable, rule-based tasks. Agentic AI handles judgment-intensive workflows — procurement approvals, compliance reviews, customer escalations, incident response — where context changes every time.
Agents don't just trigger actions — they reason about state, maintain context across steps, select tools dynamically, and produce auditable decision traces. That's what enables real workflow automation.
End-to-end agentic systems, not prompt wrappers. We handle orchestration, tool integration, memory, and governance.
Coordinator agents that decompose complex tasks and delegate to specialized subagents — each with a defined role, toolset, and authority scope. Supports hierarchical and peer-to-peer agent topologies.
Configurable escalation gates that pause workflows for human review at predefined decision points. Agents surface context, recommend actions, and resume once approved — preserving human authority where it matters.
Agents equipped with dynamic tool-use via Model Context Protocol (MCP) — connecting to your CRM, ERP, databases, communication systems, and third-party APIs without custom code per connection.
Persistent agent memory using vector stores, episodic memory, and session state management. Agents track what's been done, what failed, and what's pending — across hours or days of workflow execution.
Agentic workflows follow a goal-oriented execution loop — not a fixed script. Here's what that looks like in practice.
The orchestrator agent receives a high-level goal (from a trigger, API call, or user input), breaks it into subtasks, and determines which subagents or tools to invoke and in what order.
Subagents execute their tasks — some in parallel, some sequentially based on dependencies. Each agent uses its designated tools, retrieves relevant context from memory, and reports results back to the orchestrator.
When an agent hits an ambiguous state, a confidence threshold, or a defined policy gate, it escalates to a human reviewer with full context, recommended actions, and reasoning traces. Once approved, the workflow resumes.
Workflow results are delivered to downstream systems (Slack, email, ERP, CRM) and every decision, tool call, and escalation is logged with timestamps and reasoning for compliance and audit purposes.
The highest-value targets are workflows that are high-volume, judgment-intensive, and currently require significant human time.
RPA and scripted automation are not obsolete — but they have a ceiling. Agentic AI raises it significantly.
| Dimension | RPA / Scripts | Agentic AI |
|---|---|---|
| Handles exceptions | No — breaks or alerts human | Yes — reasons through or escalates with context |
| Adapts to change | Requires reprogramming | Adapts based on goal and context |
| Multi-system reasoning | Brittle integrations | Dynamic tool selection via MCP |
| Audit trail | Transaction logs only | Full decision reasoning and tool call traces |
| HITL integration | Manual escalation hooks only | Native, policy-driven escalation with context package |
Tell us the workflow that's costing your team the most time. We'll scope an agentic solution in our first call.
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