Agent Workflows
AI agents that act on business events with the tools, memory, approvals, and traces you can audit. Workflows that ship to production, not just demos.
Same engine — packaged for a faster start
What it is
An agent workflow is software that watches for a business event (a support ticket arrives, a sales deal updates, a deploy fails, a customer writes in) and decides what to do: gather context, call tools, propose a draft, or escalate to a human. Where a Concierge-style surface fronts a customer conversation, an agent workflow runs in the background, on its own schedule or in response to triggers, and leaves a record of what it did. That record is what separates an AI demo from an AI system you can run operations on.
Why it matters
Chat is a discovery surface, good for letting people explore what AI can do. The value organizations want from AI usually lives one layer down: in the work that happens whether or not anyone is typing. Triaging tickets, drafting responses, opening pull requests, summarizing transcripts, updating records. Each is a workflow with inputs, outputs, tools, and rules. Building those properly (state that survives restarts, retries that don't duplicate side effects, approvals where they matter, traces you can replay) is what turns AI from interesting into unattended and reliable.
What we build
Workflows triggered by events (webhooks, schedules, queues, change streams) rather than by chat. An agent that assesses the event, retrieves context through governed retrieval, calls tools through the MCP registry, and either completes the task or routes to a human approval inbox with full context attached. Durable state across model and tool calls so workflows survive restarts. Versioned graphs in source control. Production traces feeding an eval set. Promotion gates that require the eval set to pass before a change ships.
- Event triggers (webhooks, schedules, queues, change streams)
- Intent routing and owner detection
- MCP tool execution with permission scopes
- Decision, loop, and sub-workflow nodes; code and API steps
- Durable state across long-running steps
- Human approval inboxes with context
- Versioned workflow graphs — immutable draft → active → archived
- Eval-gated promotion path
How it works
We start narrow: one event type, one outcome, a small tool surface, a generous human-approval gate, and tracing from day one. We progressively remove approval gates as the eval set proves the workflow's consistency. Expanding the tool surface, removing a gate, or shipping a prompt change requires the eval set to still pass. LangGraph (LangChain) is our default substrate. When an existing stack uses the OpenAI Agents SDK, Mastra, Inngest, or Temporal, we adapt to it. The runtime layer we add is the same either way: versioning, governance, regression evals, and gated promotion. The framework decides local execution shape; the runtime decides what ships.
What it works with
Agent Workflows sits on top of the AI Platform: model calls and tool invocations go through the platform's gateway and registry. It reads from Data Foundations through governed retrieval. It writes traces that Benchmarks turns into eval sets and that Self-Optimizing Agents uses to propose variants. When a workflow involves customer-facing channels, Conversation Intelligence captures the signal and feeds it back. When a workflow gets a human correction, Closed-Loop Knowledge turns it into a knowledge update or an eval case.
Where we draw the line
Every action is traced and every high-impact decision sits behind a gate — never an unmonitored autonomous loop. We extend agent frameworks (LangGraph, OpenAI Agents SDK, Mastra) with the runtime governance they do not ship, rather than replacing them. Every event, decision, tool call, and outcome lives in the trace.
When you should start
Signals: a queue of repetitive work that follows a recognizable pattern (ticket triage, alert response, record updates, document drafting); a process humans do well but could do faster with better context assembly; a backlog of 'we should automate that' items that never got built because the automation surface was too brittle. Common starting points: support triage with permission-scoped account access, engineering workflows that take an issue and propose a PR or runbook step, or operational monitoring where the agent reads alerts, gathers correlated signals, and either resolves or escalates with a clean writeup.
Related learning
The execution engine that turns an AI agent from a chat-window demo into a long-running, event-driven, restartable process you can trust with real operations.
Approval gates that put a human in the loop where correctness, risk, or accountability actually require human judgment — designed as part of the workflow, not as a panic button bolted on after launch.
The test suite your AI workflows have to pass before any change reaches users — measuring quality, latency, cost, and safety on real production data instead of vibes.
The engine that runs an AI agent workflow as a durable, observable, restartable process instead of a one-shot script — what separates an agent demo from an agent deployment.
A governed catalog of every tool an AI agent can call — your APIs, your databases, your internal systems — with typed schemas, permission scopes, audit trails, and the standard protocol (MCP) that turns 'we exposed it to the LLM' into 'we know exactly who called what when'.
A capability in the Group e-media information AI stack. This resource connects the subject to data substrate, agent runtime, evals, and operations.