UiPath Agent Memory: Keep Context Across Automation Runs

UiPath is a robotic process automation platform that has added AI agent capabilities for workflows that require judgment and decision-making alongside automation. Carrying context between runs, what those agents remember across executions, depends on where the context is stored. A run-level variable does not persist. A queue item, an asset, or an external database does. Tools like fenbs maintain the task brief and human handoff record alongside the automation, so the people involved know what the agent is doing and where each task stands.

The approach you use for agent memory in UiPath depends on what the context is, how long it needs to last, and who else needs access to it. There is no single right answer; there are tradeoffs between simplicity, durability, and how much the context changes between runs.

Types of memory available in UiPath

UiPath offers several places to store information between runs. Each has a different scope and durability.

• In-process variables: exist only while a workflow is running. They do not persist. Use them for intermediate values within a single execution, not for context you need next time.
• UiPath Assets: key-value pairs stored in Orchestrator and available to any robot with the right permissions. Good for configuration values and simple state that changes infrequently.
• UiPath Queues: structured items that can carry data through a processing pipeline. A queue item can represent a task in progress and carry the context needed to resume or review it.
• External databases and files: any storage the robot can read and write: a SQL database, a spreadsheet, a file on a shared drive. Flexible but requires more setup and maintenance.
• AI context tools via MCP: a standard connection that lets an agent read and update a shared work record in an outside tool. Useful when the context needs to be readable by people as well as the automation.

Choosing the right approach

For simple state that does not change often, an Asset is the lowest-overhead option. For data that flows through a processing pipeline, a Queue item is more appropriate. For context that needs to survive a long period or be reviewed by a person, external storage or an MCP-connected tool makes more sense.

The key question is: who else needs access to this context? If only the robot needs it, keep it in UiPath. If a person needs to review it, approve a step, or pick up the work when the automation stops, the context needs to be in a place a person can read.

For the broader picture of agent memory design, see what is agent memory and how to build agent memory. For the comparison between agent memory and RAG (retrieval-augmented generation), see agent memory vs. RAG.

Human handoffs and the automation boundary

UiPath agents do not run forever. They hit exceptions, wait for external events, or complete a defined set of tasks. When a run ends and a person needs to continue the work, the handoff is only as good as the record the automation left behind.

A useful handoff record includes: what the agent completed in this run, what it found or changed, what is waiting for a decision or approval, and where the next run should start. Without this, the person picking up the work has to reconstruct what happened from logs.

For security considerations around what agent memory should and should not store, see agent memory security. For how MCP can be used to connect agents to an outside memory store, see agent memory MCP.

Versioning context across agent updates

As an automation evolves, the data it needs between runs may change. A context store designed for an earlier version of the workflow may not match what the updated version expects. This is a common source of silent errors: the agent reads old context and proceeds without noticing the mismatch.

Include a version field in stored context when the workflow is likely to change. When the workflow is updated, check that the context it reads is compatible with the new version. If not, either migrate the stored context or add a check that detects an incompatible version and falls back to a known safe state.

For the difference between agent memory and context windows in AI models, see agent memory vs. context. That distinction matters when deciding what to store in UiPath versus what to pass as a prompt to an AI step within a workflow.

How to do this with fenbs

1. Create a free account. Add a project for the automation process you are building.
2. Write the process goals, constraints, and human approval steps as Decisions and rules in fenbs.
3. Create a task for each automation run that needs a human handoff. The UiPath robot can update this task via the fenbs API or a connected assistant; alternatively, update it manually after each run.
4. Connect a supported AI assistant at /integrations for sessions where a person and an AI assistant are reviewing the automation output together.
5. Before ending a maintenance session, save Where we left off so the next review starts from the current state of the automation.

fenbs does not replace UiPath's built-in memory features. It provides the human-readable record alongside the automation: what the agent is doing, what has been decided, and what needs a person's attention. The automation state stays in UiPath; the project record stays in fenbs.