Week of 15 August 2026
From Agent Portfolios to Connected Workflows
Graph Engineering, and why the next stage of enterprise AI is coordinating the agents you already have
AI adoption is expanding faster than workflow ownership
The first wave of enterprise AI was built by individuals. Employees inside Microsoft Copilot, Copilot Studio and Google Gemini built assistants to search documents, summarise information, draft content and review data, and some teams went on to run several of them for one area of work. One person can coordinate several agents through tacit knowledge. Once the workflow crosses operations, technology, risk, compliance, finance and client teams, the organisation needs explicit ownership, handoffs and control points.
A signal from the agent-building community
Two words are doing all the work in that conversation, so here they are, drawn. Once you can see the difference between them, the rest of this briefing is about one of them.
One agent, going round
An agent is given a task and works at it in a cycle: it tries something, checks its own result, and tries again until it decides the task is done. Everything happens inside one box, and one person watches that box. Almost every AI assistant in your organisation today works this way.
Many steps, joined up
Draw several of those boxes and the connections between them and you have a graph. Each box is a step, whether an agent, a person or a system does it. Each arrow is a handoff. Some steps are approvals, and some arrows go backwards, returning the work to an earlier step or escalating it to a specialist.
Graph Engineering is the practice of drawing that second picture for a piece of work that matters, and then governing it: deciding which steps exist, who owns each one, where approval is required, and what happens when the work has to go backwards.
“Are we still talking loops or did we shift to graphs yet?”
Creator of OpenClaw, now working for OpenAI
The signal is that the people building these systems have stopped talking about the first picture and started talking about the second one. That is all it is. It does not show that graphs are universally better, or that the term has settled into a standard. What it marks is a change of subject, and the same change is now arriving inside Microsoft and Google's enterprise products.
Most organisations are somewhere between an inventory and a portfolio
The third stage is the one that carries a business outcome, and it is the one almost nobody has reached.
Agent inventory
You know which agents exist and where they are being used.
Agent portfolio
Development is standardised, permissions are managed, acceptable use is defined, and someone decides which agents deserve further investment.
Connected workflow
Agents, systems, employees, approvals, evidence and exception paths work together across boundaries, under one business outcome.
Where the difficulty starts
Portfolio language can hide this: launched use cases, trained employees and a governance committee are real signals of investment, and none of them is evidence that work crosses the organisation reliably. The distance that matters is between knowing where your agents are and connecting them into work the organisation can govern.
Graph Engineering makes hidden workflow dependencies visible
A single reporting workflow, drawn as the business experiences it. Data arrives from internal systems, two agents do real work inside it, and people decide, review and approve around them.
- Step 1
Pull and clean the data
Figures are extracted from the finance system and prepared for use.
Who IT and operations
Runs on Client database
- Step 2
Draft the first read
An agent works through the data and writes the quantitative commentary.
Who Analyst with the agent
Runs on Copilot agent, reporting
- Step 3
Decide what to act on
A person reviews the insights and confirms which actions are required.
Who Analyst
Runs on Human judgement
- Step 4
Build the dashboard
A second agent turns the confirmed insights into a specification and a working preview.
Who Analyst with the agent
Runs on Copilot agent, build
- Step 5
Check and circulate
Dashboard quality and data consistency are reviewed before anything is sent.
Who Leadership review and compliance
Runs on Email and Teams
The organisation describes these as separate use cases. The business experiences one workflow.
Every arrow between two adjacent steps is a handoff, and each one raises the same four questions: does the complete process have a clear owner, are the handoffs controlled, is there visible evidence of what happened, and is there a reliable path when an exception appears. Those questions are unanswerable while the steps are managed as separate projects.
Illustrative. An anonymised design from a private-bank discovery conversation; deployment is unconfirmed.
Microsoft and Google are making agents easier to connect
Both vendors are shipping the connective layer, and it is worth knowing what has already arrived in products your organisation may be licensing today.
Copilot Studio agent flows
Flows that can be started by an event, a schedule, or another agent, and that run across your connected systems.
- Connectors
- Branching
- Loops
- Human approvals
- Child flows
- Monitoring
Gemini Enterprise
The same direction from the other side: agents that delegate to other agents, under a shared identity and governance layer.
- Graph-based sub-agent orchestration
- Agent-to-agent delegation
- Identity
- Governance
- Observability
A platform can connect two agents. It cannot tell you who owns the result.
Ownership, data access, exception approval and stopping conditions stay organisational decisions. This is why the useful skill is identifying which connections a consequential workflow actually depends on, and governing those, rather than connecting everything that can be connected.
Leaders should build a portfolio of owned, bounded workflows
An agent owner is responsible for one agent: its purpose, tools, boundaries and evaluation. Several agents contributing to one outcome need something above that.
A workflow owner
Accountable for the end-to-end business result, across every department the work passes through.
A control owner
Defines the permissions, approvals, evidence, escalation and rollback the workflow has to satisfy.
A platform owner
Provides identity, reusable interfaces, monitoring, versioning and lifecycle standards, so each team is not inventing its own.
Start with one consequential workflow and five questions
Deliberately small. Choose one workflow that matters, such as the reporting case above, and answer these before connecting anything.
What outcome is the workflow responsible for?
Name the business result, not the tooling. If it cannot be stated in a sentence, the workflow is drawn too wide.
Which agents, systems and people participate in it?
The full list, including the steps that are entirely human and the systems nobody thinks of as AI.
Where do approvals, exceptions and human escalation occur?
The control points. Mark where the work is allowed to stop, and who is allowed to stop it.
Who owns the end-to-end result?
One name. Owners of individual steps do not add up to an owner of the outcome.
What evidence would justify expanding the workflow?
Decide this before the pilot runs, so the answer is not written backwards from whatever the pilot produced.
Workflow improvement requires measures of changed work
A pilot should be judged on whether the work itself changed. The easy numbers describe how much AI is present, which is a different question.
What leaders should take away
The next phase of enterprise AI will be decided by whether organisations can connect the right capabilities around the right workflows while keeping responsibility visible.