RPA to Agentic AI Migration Guide: Upgrade Your Automation Workforce in 2025

Your RPA investment was the right call in 2018. The bots delivered. They moved data between systems, processed structured transactions, and eliminated keystrokes from high-volume workflows. The ROI was real.

But the operating environment has changed. Your processes are more dynamic. Your data is less structured. Your exception queues are growing. And the maintenance line item on your RPA portfolio is quietly consuming the efficiency gains those bots were supposed to deliver.

This is the RPA ceiling—and most organizations hit it without realizing it because they've normalized the cost of keeping brittle bots alive.

This guide is built for COOs and CTOs who have already invested in RPA and are asking the right next question: How do we upgrade from rule-based automation to an intelligent, autonomous agent workforce—without burning down what we've already built?

What follows is a practitioner-level migration playbook. It covers when to migrate, what to migrate, how to migrate, and how to measure success. It is deliberately vendor-agnostic on the technology layer, but direct about the commercial reality: organizations that replace RPA with AI agents on targeted processes are compressing labor overhead by 60–80%. Those that keep patching bots are subsidizing technical debt.

The window for first-mover advantage in agentic operations is open. This guide helps you walk through it.

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Why Your RPA Investment Is Approaching Its Ceiling

RPA delivered on its promise—for the automation landscape of 2015. Screen-scraping bots that execute deterministic task sequences were a legitimate breakthrough for organizations drowning in manual, repetitive work. But the enterprise operating environment has fundamentally shifted, and the limitations of rule-based automation are no longer theoretical. They are showing up in your P&L.

The maintenance tax is real. Industry benchmarks consistently show that 30–50% of enterprise bot portfolios break annually due to UI changes, process updates, and system upgrades. Every time a vendor updates a portal layout, every time an internal system migrates to a new version, bots fail. The cost of diagnosing, repairing, and redeploying those bots is not trivial—it is a recurring line item that compounds year over year.

This is the executive blind spot: organizations that treat RPA maintenance as "normal overhead" are not maintaining a capability. They are subsidizing technical debt. The maintenance budget is not preserving value—it is preventing collapse.

The workflow mismatch is growing. Modern enterprise workflows are dynamic, judgment-driven, and increasingly dependent on unstructured data. Customer communications arrive as free-text emails. Invoices come in inconsistent formats. Exceptions require contextual reasoning, not rule lookups. RPA's rigid, deterministic execution model cannot handle this. It was never designed to.

Contrast this with what the market is moving toward. Organizations migrating to agentic AI are deploying autonomous agents that reason through exceptions, adapt to novel inputs, and deliver outcomes—not just execute clicks. Early adopters on targeted processes are reporting 60–80% compression in labor overhead, not because agents are faster at clicking, but because agents eliminate the exception queues, the human-in-the-loop escalations, and the maintenance cycles that consumed the efficiency gains RPA was supposed to deliver.

RPA was the right tool for 2015-era automation. Agentic AI is the right tool for 2025-era operations. The question is not whether to migrate—it is how fast you can execute.

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RPA vs. Agentic AI: A Definitive Capability Comparison

Before designing a migration strategy, executives need a precise understanding of what they are migrating from and what they are migrating to. The conflation of RPA, AI, and "intelligent automation" in market messaging has created confusion. Let's cut through it.

RPA, defined precisely: Robotic Process Automation deploys software bots that mimic human interactions with digital interfaces. These bots execute deterministic task sequences—click here, copy this, paste there—based on pre-programmed rules. They have zero contextual reasoning. They cannot interpret ambiguity. They operate by navigating UI elements at the pixel level, which makes them inherently brittle to interface changes.

Agentic AI, defined precisely: Agentic AI deploys LLM-backed autonomous agents that can plan multi-step tasks, handle exceptions through reasoning, use tools (APIs, databases, even RPA bots), and adapt to novel inputs without reprogramming. An agent does not follow a script—it pursues an outcome within defined guardrails.

Side-by-Side Comparison

The Fragility Tax

The most important architectural difference is what each approach couples to. Every RPA bot is coupled to a specific UI state—a particular button location, a specific field label, a defined page flow. When that state changes, the bot breaks. This coupling creates a compounding fragility tax that grows with portfolio size.

Agentic AI agents are coupled to outcomes, not interfaces. An agent tasked with processing an invoice doesn't navigate a screen—it reads the document, extracts relevant data, validates against business rules, and posts to the system of record via API. If the invoice format changes, the agent adapts. If the ERP interface updates, the API layer absorbs it.

The Cognitive Gap

Consider a straightforward scenario: an ambiguous email arrives from a supplier with a partial invoice reference, a request for payment status, and a question about updated shipping terms. An RPA bot cannot process this. It cannot read the email, determine intent, cross-reference the invoice, check shipment status, and compose a response. It would route the entire interaction to a human queue.

An agentic AI agent can reason across the email content, query relevant systems, determine the appropriate response, and either resolve autonomously or escalate with full context. This is not a marginal improvement—it is a category shift.

Where RPA Still Wins

Credibility demands nuance. RPA remains the right tool for a narrow but real category of work: high-volume, perfectly stable, pixel-perfect tasks in environments where the UI never changes and the process has zero variability. Mainframe data entry in legacy systems with frozen interfaces is a legitimate RPA use case. But that category is shrinking, not growing.

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The Four Migration Triggers: When It's Time to Upgrade RPA to Agents

Migration decisions should be driven by business signals, not technology hype. Here are the four triggers that indicate your RPA portfolio is ready for agentic AI migration—each anchored to a financial or operational outcome metric.

Trigger 1: Maintenance Costs Exceed 25% of Initial Implementation Cost Annually

When you are spending more than a quarter of the original build cost every year just to keep bots running, the bot portfolio is approaching net-negative ROI. This threshold is the clearest financial signal that the automation is generating less value than it consumes in upkeep. Track this number per bot and in aggregate.

Trigger 2: Exception Rates Above 15%

RPA's value proposition is straight-through processing. When more than 15% of transactions require human-in-the-loop handling due to exceptions the bot cannot process, the efficiency gain evaporates. You are not automating the process—you are automating the easy part and staffing the hard part. Agents are designed to reason through exceptions, which is precisely where RPA fails.

Trigger 3: Process Velocity Mismatch

If your business processes change faster than your bots can be reprogrammed, you are caught in a permanent rebuild cycle. This is common in organizations undergoing digital transformation, ERP migrations, or rapid product evolution. Agents absorb process variability by design; bots require explicit reprogramming for every change.

Trigger 4: Unstructured Data Ingestion Requirements

Any workflow that touches emails, PDFs, voice recordings, images, or free-text inputs exceeds RPA's native capability. If you have added OCR layers, NLP preprocessors, or manual triage steps to feed structured data to your bots, you have already acknowledged RPA's limitation—you are just compensating for it with duct tape.

Self-Assessment Scoring Framework

Score each bot portfolio on these four triggers using a 1–5 scale:

Total score 16–20: Migrate now. 12–15: Migrate next quarter. 8–11: Evaluate in 6 months. 4–7: Retain as RPA or retire.

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Pre-Migration Audit: Mapping Your RPA Portfolio for Agent Readiness

Before migrating anything, you need a clear, honest picture of what you have, what it costs, and what it's worth. The pre-migration audit is a five-step diagnostic that produces a prioritization matrix your team can execute against.

Step 1: Bot Inventory Classification

Catalog every active bot in your portfolio. For each, capture:

• Process type (transactional, document processing, data migration, reporting)
• Exception rate (percentage of runs requiring human intervention)
• Maintenance frequency (repairs per quarter)
• Business criticality (revenue impact if the process stops)

Most organizations discover that 20–30% of their bot portfolio is either dormant, redundant, or so fragile that it generates more support tickets than value. Identifying these bots early creates an immediate cost recovery opportunity through retirement.

Step 2: Process Complexity Scoring

Score each automated process on a 1–5 matrix across five dimensions:

Processes scoring 15+ are strong candidates for agentic AI migration. Processes scoring below 10 with low exception rates may be appropriate to retain as RPA.

Step 3: ROI Recapture Modeling

For each migration candidate, calculate three scenarios:

• Current bot TCO at 12, 24, and 36 months (including maintenance, exception handling labor, and downtime cost)
• Projected agent TCO at the same intervals (including deployment, monitoring, and per-transaction cost)
• Net ROI recapture — the delta between the two

This modeling exercise often reveals that the most expensive bots are not the most complex—they are the ones with the highest exception rates and most frequent maintenance cycles.

Step 4: Dependency Mapping

Identify every upstream and downstream system each bot touches. Map which integrations can be reused as agent tools (most API-based integrations can) versus which require replacement (screen-scraping connections to systems that now offer APIs). This mapping directly affects migration timeline and cost.

Step 5: Governance and Compliance Inventory

Flag any process with audit trail requirements, data residency constraints, or regulatory obligations. These requirements do not block migration—agentic AI architectures can satisfy them with immutable reasoning logs and policy constraints—but they must be explicitly designed into the agent architecture from day one, not retrofitted.

Prioritization Matrix Output

The audit produces a four-category classification for every bot:

• Migrate Now — High complexity, high maintenance, high business value
• Migrate Next — Medium complexity, rising maintenance trend
• Retire — Low value, high maintenance, no strategic relevance
• Retain as RPA — Low complexity, stable, cost-effective as-is

Critical insight: Retiring low-value bots is a legitimate and often overlooked cost recovery lever. Not every RPA bot warrants migration. Some simply warrant a shutdown.

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The Migration Architecture: How Agentic AI Replaces and Extends RPA Workflows

Migration from RPA to agentic AI is not a rip-and-replace of your enterprise systems. It is a re-platforming of the automation layer—replacing brittle, script-based bots with intelligent, outcome-driven agents that integrate with your existing infrastructure.

Three Migration Patterns

Pattern 1: Full Replacement. The agent absorbs the entire workflow end-to-end. This is appropriate for processes where RPA was already handling the full scope but with high exception rates and maintenance burden. The agent takes over all task execution, decision-making, and system interactions.

Pattern 2: Augmentation. The agent handles exceptions and decisions while RPA continues handling stable, deterministic sub-tasks. This is the right pattern when parts of the workflow are genuinely well-served by RPA (high-volume, zero-variability data entry) but exception handling and decision logic are consuming human bandwidth.

Pattern 3: Orchestration. The agent acts as the workflow orchestrator, delegating tasks to RPA bots, APIs, databases, and other tools as needed. The agent reasons about what needs to happen and routes execution to the right tool. In this model, your existing RPA bots become specialized instruments within an agent's toolkit—not the other way around.

The Agent Architecture Stack

Enterprise-grade agent deployments typically consist of four layers:

• Reasoning Layer (LLM): The cognitive engine that interprets inputs, plans actions, and makes decisions within defined guardrails.
• Tool Layer: APIs, RPA bots, databases, document processors, and any other system the agent can invoke to execute tasks. Existing ERP, CRM, and ITSM integrations are typically reusable as agent tools—migration does not require ripping out systems of record.
• Memory Layer: Context storage, conversation history, audit logs, and learned patterns that allow the agent to maintain state across complex workflows.
• Orchestration Layer: Workflow routing logic that determines task sequencing, parallelization, escalation paths, and handoff protocols.

From Bot Maintenance to Outcome Delivery

The organizational model shift is as important as the technical one. RPA portfolios are managed as collections of individual scripts—each requiring its own maintenance, monitoring, and repair cycle. An agent workforce is managed as a capability: a team of autonomous agents with defined roles, performance metrics, and accountability structures.

This reframing changes the operating model from "how many bots are running" to "how many outcomes are being delivered." It changes the cost model from platform licensing and developer hours to cost-per-transaction and outcome yield.

Accountability Infrastructure

Unlike RPA logs that capture clicks and screenshots, agentic AI agents generate reasoning traces—detailed records of what the agent considered, why it chose a specific action, and what outcome it produced. These decision logs satisfy enterprise audit requirements in ways that RPA activity logs never could, because they capture the why behind every action, not just the what.

meo's pay-for-performance model adds a commercial accountability layer. Clients invest against delivered outcomes, not infrastructure or platform fees. This eliminates the deployment risk that makes traditional migration business cases difficult to approve—if the agents don't deliver, the cost doesn't accrue.

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Step-by-Step RPA to Agentic AI Migration Playbook

This playbook is designed for a single process migration. Execute it once, prove the model, then scale.

Phase 1: Process Decomposition (Weeks 1–2)

Break the target RPA workflow into its atomic components:

• Tasks: Every discrete action the bot performs
• Decision points: Every branch in the workflow logic
• Exception paths: Every scenario that currently routes to a human queue
• Data inputs/outputs: Every piece of information consumed or produced

Document the process as it actually operates, not as the original RPA spec describes it. Process drift is common—bots often diverge from their design documentation over time.

Phase 2: Agent Design (Weeks 3–4)

Before writing a single line of code, define:

• Agent role: What outcome is this agent accountable for?
• Tool set: Which systems, APIs, and data sources does the agent need access to?
• Guardrails: What is the agent explicitly not allowed to do?
• Escalation logic: Under what conditions does the agent hand off to a human?
• Success metrics: What does "done well" look like, quantified?

This design phase is the most important investment in the entire migration. An under-specified agent design leads to scope creep, hallucination risk, and governance gaps. Invest the time here.

Phase 3: Parallel Run (Weeks 5–8)

Operate the agent and the existing RPA bot simultaneously on live traffic. Route identical inputs to both. Measure:

• Exception rate (agent vs. bot)
• Accuracy (agent vs. bot)
• Throughput (agent vs. bot)
• Cost per transaction (agent vs. bot)

The parallel run produces the empirical evidence needed for a confident cutover decision. It also surfaces edge cases that neither the RPA bot nor the agent design anticipated.

Phase 4: Cutover Decision Gate (Week 9)

Use parallel run data to make a data-driven go/no-go decision. Define thresholds in advance:

• Agent accuracy must meet or exceed bot accuracy
• Agent exception rate must be lower than bot exception rate
• Agent cost-per-transaction must project favorably at 12-month scale

This is a data-driven gate, not a calendar-driven one. If the data doesn't support cutover, extend the parallel run. Do not force a migration that hasn't proven itself.

Phase 5: Full Deployment and Monitoring (Weeks 10–12)

Deploy the agent as the primary workflow operator. Establish real-time dashboards tracking:

• Task completion rate
• Error rate
• Cost-per-transaction
• Escalation volume
• Reasoning trace quality

Embed governance checkpoints at every phase: Who approves the agent's decision boundaries? Who owns escalations? How are audit logs retained? These questions have answers—but they must be answered explicitly, not assumed.

Practical tip: Migrate one process end-to-end before scaling. The temptation to migrate the entire bot portfolio simultaneously is real and should be resisted. The first migration builds organizational muscle, surfaces integration issues, and establishes the governance framework that all subsequent migrations will use.

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Common Migration Risks and How to Neutralize Them

Migration risk is real. It is also manageable. Here are the five risks that derail enterprise RPA-to-agent migrations—and the specific mitigations that neutralize each.

Risk 1: Hallucination in Structured Data Tasks

LLMs can generate plausible but incorrect outputs. In a financial transaction workflow, this is unacceptable.

Mitigation: Use deterministic tool calls for structured data operations—the agent reasons about what to do, but executes via validated API calls, not generated outputs. Add output validation layers that cross-check agent results against source data. Implement confidence thresholds: if the agent's confidence falls below a defined level, it escalates rather than guesses.

Risk 2: Compliance Gaps in Regulated Industries

Regulated processes require audit trails, data residency compliance, and access controls that must survive the migration.

Mitigation: Deploy immutable reasoning logs that capture every agent decision with full traceability. Implement role-based access controls on agent capabilities. Align agent policy constraints to regulatory frameworks (SOC 2, HIPAA, GDPR) during the design phase, not after deployment.

Risk 3: Stakeholder Resistance from IT Teams with RPA Expertise

Teams that built and maintain the RPA portfolio may perceive agentic AI migration as a threat to their roles.

Mitigation: Reframe agent operations as a higher-leverage version of automation management. The skills that made someone an effective RPA developer—process analysis, exception handling design, system integration—are directly transferable and more valuable in the agentic AI context. Invest in reskilling early and visibly.

Risk 4: Scope Creep in Agent Capability Design

Without clear boundaries, agents can be asked to do too much, leading to unreliable performance and governance gaps.

Mitigation: Define outcome-scoped agent mandates. Each agent has a specific job to do, clear success metrics, and explicit boundaries on its authority. Agents are given a job, not unlimited agency.

Risk 5: Vendor Lock-in in Agentic Platforms

Mitigation: Make architecture decisions that separate the reasoning layer (LLM) from the integration layer (tools, APIs, data). This ensures that the LLM provider can be swapped without rebuilding the entire agent infrastructure.

The larger strategic risk is not migration failure—it is migration delay. Organizations that over-index on risk and defer migration are compounding their RPA technical debt while competitors build agentic capability.

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Measuring Migration Success: The Metrics That Matter to the C-Suite

The C-suite does not care about bot uptime or agent architecture. They care about cost, speed, and risk. Structure your migration reporting accordingly.

Primary Metric: Cost-Per-Transaction

Before and after migration. This is the number that closes boardroom conversations. If your RPA cost-per-transaction for invoice processing was $4.20 and your agent cost-per-transaction is $0.85, the conversation is over.

Secondary Metrics

• Exception rate reduction: Percentage decrease in transactions requiring human intervention
• Straight-through processing rate: Percentage of transactions completed with zero human touch
• Mean time to resolution: Average time from task initiation to outcome delivery
• Bot maintenance cost eliminated: Direct savings from retiring RPA maintenance overhead

Introducing Agent Yield

Agent yield is the ratio of autonomous completions to total tasks initiated, expressed as a percentage. A well-deployed accounts payable agent should target 90%+ yield. A complex compliance review agent might target 70%+ yield with structured escalation on the remainder. Track yield by process category and set improvement targets quarterly.

From Activity Logs to Outcome Dashboards

RPA reporting tells you what happened: "Bot clicked 47 buttons and processed 312 records." Agentic AI reporting tells you what was achieved: "Agent resolved 298 of 312 invoices autonomously at $0.85 per transaction with 99.2% accuracy. 14 escalated to human review with full context."

This shift—from logging activity to measuring achievement—is the reporting transformation the C-suite is looking for.

meo's pay-for-performance model makes ROI verification structural. Clients can structure contracts around cost-per-transaction targets, ensuring that the metrics dashboard is not just a reporting tool—it is the commercial accountability mechanism.

Recommend a 90-day post-migration review cadence with defined thresholds for expanding agent scope, adjusting guardrails, or escalating issues.

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Migration Case Patterns: Process Categories with the Highest ROI

Not all processes are equal migration candidates. These five categories consistently deliver the highest ROI when migrated from RPA to agentic AI.

Accounts Payable and Invoice Processing

RPA failure mode: Invoices arrive in inconsistent formats—different vendors, different layouts, different data fields. RPA bots trained on specific templates break when formats vary, generating massive exception queues.

Agent advantage: Agents read and interpret invoices regardless of format, extract relevant data, validate against purchase orders, and post to the ERP. Documented deployments achieve 90%+ straight-through processing, compared to 60–70% typical for mature RPA implementations.

Customer Onboarding and KYC

RPA failure mode: Document-heavy, judgment-intensive workflows. Identity documents vary by country, format, and quality. RPA cannot assess document authenticity or make risk-based decisions.

Agent advantage: Agents reason across document types, cross-reference data sources, flag anomalies, and make preliminary risk assessments—reducing human review to exception cases only.

IT Service Desk and Incident Triage

RPA failure mode: Ticket classification and routing is rule-based and brittle. When tickets don't match predefined categories, they sit in queues.

Agent advantage: Agents reason across ticket history, knowledge bases, and system state to diagnose and resolve L1/L2 tickets autonomously. They can also escalate with full diagnostic context, reducing L3 resolution time.

HR Operations

RPA failure mode: High-volume processes like offer letter generation, benefits enrollment, and employee queries have enough variability to generate frequent exceptions but not enough complexity to justify dedicated human teams.

Agent advantage: Agents handle the variability—different offer terms, different benefits scenarios, different employee questions—while maintaining compliance with HR policies and generating audit-ready documentation.

Supply Chain Exception Management

RPA failure mode: Supply chain disruptions require reasoning across multiple data sources simultaneously—shipment trackers, inventory systems, supplier communications, demand forecasts. RPA can monitor a single data source but cannot synthesize across them.

Agent advantage: Agents monitor, diagnose, and initiate resolution across multiple data sources, escalating only when the disruption exceeds defined thresholds or requires commercial negotiation.

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How to Build the Business Case for RPA to AI Migration

Frame the migration as a capital reallocation decision, not a technology project. The money is already being spent—it's going to RPA maintenance, exception handling labor, and opportunity cost. The business case redirects that spend toward higher returns.

Three-Part Business Case Structure

Part 1: Current State Cost Baseline

• Total RPA platform licensing and infrastructure
• Bot maintenance and repair labor (internal and vendor)
• Human-in-the-loop exception handling cost
• Opportunity cost of processes that cannot be automated by RPA

Part 2: Migration Investment and Break-Even Timeline

• Agent design and deployment cost (per process)
• Parallel run operational cost
• Reskilling and change management investment
• Projected break-even point (typically 4–8 months for high-exception processes)

Part 3: Steady-State Economics

• Projected cost-per-transaction at 12 and 36 months
• Maintenance cost trajectory (declining, unlike RPA)
• Capacity gained from exception handling labor reallocation

Quantify the "Do Nothing" Risk

RPA technical debt compounds. Each quarter of delayed migration increases the future migration complexity, because more bots are built on the old model, more exceptions accumulate, and more institutional knowledge about workarounds becomes embedded in tribal knowledge rather than system logic. The cost of inaction is not zero—it is the compounding maintenance cost plus the competitive gap.

Address CFO Objections Directly

Migration is not a rip-and-replace. It is a phased reallocation that generates measurable returns at each phase gate. The parallel run model means no process goes dark during migration. The phased approach means capital is deployed incrementally, with decision gates at every stage.

meo's pay-for-performance model simplifies the business case fundamentally. When clients only pay for delivered outcomes, the financial model shifts from "projected ROI" to "contractual ROI." The risk of underperformance is absorbed by the deployment partner, not the client. This changes the CFO conversation from "what if it doesn't work" to "what do we gain when it does."

Executive Summary Template

Structure your board-level summary as:

1. Current state: What we spend on RPA today (total and per-process)
2. Problem: Maintenance and exception costs are compounding; bot ceiling is reached
3. Proposal: Phased migration of [X] highest-cost processes to agentic AI
4. Investment: $[X] over [Y] months, funded by RPA maintenance reallocation
5. Return: [X]% cost-per-transaction reduction, [X]% exception rate reduction
6. Risk mitigation: Parallel run validation, phased deployment, pay-for-performance commercial model

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Start Your Migration: Next Steps with meo

meo deploys AI agents as a scalable, accountable workforce. Our pay-for-performance model means you do not absorb platform cost until agents prove outcomes on your processes, with your data, in your environment.

This is not a technology project. It is a workforce transformation—replacing the labor overhead of maintaining brittle RPA bots with autonomous agents that deliver measurable business results.

Your Entry Point: The RPA Portfolio Audit

The first concrete step is a portfolio audit and prioritization workshop. We assess your current bot inventory, score each process for agent readiness, model the ROI recapture opportunity, and deliver a prioritized migration roadmap with clear phase gates and financial projections.

Take Action Now

→ Schedule a Portfolio Audit — A 90-minute working session with our team to assess your RPA portfolio and identify the highest-ROI migration targets.

→ Download the RPA Migration ROI Calculator — A structured model to quantify your current RPA total cost of ownership and project agent economics.

Organizations that migrate now are compressing labor overhead while competitors maintain it. The agentic AI capability gap is forming today—and the cost of closing it later will be higher than the cost of leading now.

The window for first-mover advantage in agentic operations is open. It will not stay open indefinitely.