Businesses have spent years investing in software to make work easier, yet a considerable part of the working day still involves operating that software. Employees search for records, update fields, move information between applications and work out which screen contains the answer they need. Even when each application is reasonably good, the person using it often becomes responsible for joining everything together.
We think this is likely to change substantially. Our expectation is that AI agents will become the primary way people interact with software for many routine business tasks. Employees will describe what they want to achieve, while agents retrieve information, use the appropriate tools and carry out work across the systems underneath.
That future will develop unevenly, and there are good reasons for people to retain direct control over certain tasks. Nevertheless, it has practical implications for businesses today. The quality of your data, the structure of your processes and the accessibility of your applications could soon have a much greater influence on how effectively your team works.
From navigating applications to asking for outcomes
From navigating applications to asking for outcomes
Consider what happens when an account manager needs to prepare for a customer meeting. They might open the CRM to review recent conversations, check the accounting system for outstanding invoices, look through support tickets and visit the project management platform to understand how ongoing work is progressing.
The information is already available, but gathering it requires the employee to know where to look and how to interpret what they find. A straightforward request becomes a small research exercise across several applications.
An agent with suitable access could potentially assemble that information from a single instruction:
“Prepare me for tomorrow’s meeting with this customer. Summarise recent activity, outstanding issues and anything we need to discuss.”
The more significant opportunity comes afterwards. Having reviewed the summary, the employee could ask the agent to prepare an agenda, create an internal follow-up task or draft a message to the project manager. With the appropriate tools and permissions, the agent could carry out those actions in the relevant systems.
AI systems can already be designed to use tools and choose steps towards an objective. Anthropic’s guidance on building effective agents describes this distinction between predefined workflows and agents that determine how to proceed as they work. That capability provides a foundation for the change we expect, although dependable business use still requires careful implementation. anthropic.com
The employee’s attention can then move towards the customer and the decisions that need making, with less time spent navigating software to gather the necessary context.
Software becomes the infrastructure behind the work
Software becomes the infrastructure behind the work
If agents become the main point of interaction, the applications underneath them will continue to perform essential functions. Accounting software will maintain financial records, the CRM will organise customer relationships, and project management systems will hold tasks, responsibilities and deadlines.
What changes is how often an employee needs to open each application directly.
We can imagine a working day in which someone reviews priorities through an assistant, asks it to investigate an issue and approves a proposed action without visiting every system involved. Some tasks may begin through a conversation, while others start automatically when an agreed condition is met.
A renewal process, for example, could bring together contract dates, current services, account activity and outstanding issues before presenting a proposed renewal for review. The person responsible would still make the commercial decision, but much of the preparation could happen in the background.
This would also change what businesses value when choosing software. The interface would remain relevant, but buyers would have stronger reasons to examine the capabilities underneath it: whether information can be retrieved reliably, whether actions can be performed through integrations, and whether access can be restricted appropriately.
A feature that is available only through a sequence of manual screens may be much harder to incorporate into an agent-led process.
Interfaces will still have a role
Interfaces will still have a role
We expect businesses to spend less time navigating application interfaces, but that does not mean every task should become a conversation.
A table can be a better way to compare hundreds of records. A project board can make workload easier to understand. A chart can reveal a trend more clearly than several paragraphs of explanation. People will also need ways to inspect evidence, review proposed changes and investigate mistakes.
The likely direction is a combination of agents and interfaces, with the agent bringing forward the view needed for a particular decision. Someone might ask which projects are falling behind and receive an interactive table showing deadlines, blockers and responsible team members. They could examine the details before asking for follow-up actions.
In that environment, interfaces become more focused on helping people understand and control the work. Routine navigation and administration can increasingly happen behind the scenes.
Why MCP matters to this future
Why MCP matters to this future
For agents to work across business applications, they need a reliable way to access information and perform actions. Model Context Protocol, or MCP, provides an open standard for connecting AI applications to external tools and data sources. An MCP server can expose specific capabilities that a compatible AI application can discover and use. Model Context Protocol
We explored the underlying technology in our earlier article, What is MCP, and what does it mean for businesses?. Its relevance here is that a common connection standard can make business systems more accessible to agents.
For example, a connection might expose tools for finding customer records, retrieving invoice information or creating tasks. The agent can request those operations using defined inputs, while the connected system performs the underlying action. The actual capabilities depend on what the integration makes available. Model Context Protocol
Businesses should therefore look beyond whether a supplier says it “supports MCP”. A connection that retrieves a small selection of information offers a different opportunity from one that supports the complete process you want to improve.
It is also worth examining existing APIs and integration options. MCP often provides an additional layer over those capabilities, so the accessibility of the underlying application remains important. Businesses can start evaluating these foundations now without committing themselves to a particular agent platform.
Good data hygiene becomes an operational requirement
Good data hygiene becomes an operational requirement
An employee can often work around poor data because they know the business. They recognise that two slightly different company names refer to the same customer, understand which spreadsheet is current and remember that a supposedly active contract ended last month.
An agent may have no dependable way to resolve those inconsistencies. If it finds two customer records with different renewal dates, it needs an authoritative source or a defined process for handling the conflict.
This makes good data hygiene particularly important when AI moves from producing answers to performing actions. An incorrect email address might lead to a message being sent to the wrong person. An outdated service record could affect a renewal proposal. An ambiguous account status could trigger an inappropriate follow-up.
Preparing data for agents therefore involves more than removing duplicates. Businesses need consistent fields, meaningful statuses, clear relationships between records and an agreed source of truth for each important type of information.
Customer identities should be linked reliably across systems. Dates and currencies should be explicit. Superseded documents should be distinguishable from current ones. Important decisions should be recorded somewhere accessible rather than remaining entirely in someone’s inbox or memory.
These improvements benefit people immediately. They also make it easier to introduce automation with confidence because there is less ambiguity for either an employee or an agent to interpret.
Processes need to be clear enough to delegate
Processes need to be clear enough to delegate
Clean data alone will not explain how your business should operate.
An instruction such as “follow up overdue customers” sounds simple until someone needs to decide what counts as overdue, whether disputed invoices are excluded, who owns the relationship and which customers require a more sensitive approach.
Employees often learn these rules informally. Delegating the work to an agent requires the business to make them explicit.
That means defining the intended outcome, the information required, the actions allowed and the situations that need human judgement. It also means accepting that some parts of a process are better handled by conventional automation. A fixed calculation or validation rule can remain in software, while an agent helps gather context or manage a less predictable sequence of tasks.
For many businesses, documenting those boundaries will be one of the most useful parts of preparing for agents. It exposes assumptions and inconsistencies that may already be causing problems.
How businesses can get ahead now
How businesses can get ahead now
The strongest starting point is a specific piece of work that repeatedly consumes time. Meeting preparation, internal reporting and finding information across applications are useful candidates because the results can be checked before the agent is given permission to change anything.
We would approach preparation in five stages:
| Stage | Practical action |
| Identify the work | Choose a recurring task with a clear outcome and record how much time it currently takes. |
| Improve the data | Resolve duplicates, standardise key fields and agree which system holds each authoritative record. |
| Check connectivity | Assess relevant APIs, MCP connections, available actions and permission controls. |
| Define responsibility | Decide what the agent can do independently and when a person needs to review or approve it. |
| Test and measure | Compare results with the existing process, including errors, checking time and integration costs. |
Access should follow the needs of the task. An agent preparing an account summary may need permission to read selected information, while an agent updating customer records requires a different level of control. MCP documentation describes mechanisms such as tool availability settings, approval dialogues and activity logs, but businesses still need to implement and configure suitable controls. Model Context Protocol
Testing should include the awkward cases: missing information, conflicting records, unavailable applications and requests the agent is not authorised to complete. A useful system needs to recognise those situations and make them visible.
The measure of success is the improvement to the complete process, including the time people spend checking the agent’s work. A task completed quickly has limited value if it creates a larger verification burden afterwards.
Processes need to be clear enough to delegate
Processes need to be clear enough to delegate
Clean data alone will not explain how your business should operate.
An instruction such as “follow up overdue customers” sounds simple until someone needs to decide what counts as overdue, whether disputed invoices are excluded, who owns the relationship and which customers require a more sensitive approach.
Employees often learn these rules informally. Delegating the work to an agent requires the business to make them explicit.
That means defining the intended outcome, the information required, the actions allowed and the situations that need human judgement. It also means accepting that some parts of a process are better handled by conventional automation. A fixed calculation or validation rule can remain in software, while an agent helps gather context or manage a less predictable sequence of tasks.
For many businesses, documenting those boundaries will be one of the most useful parts of preparing for agents. It exposes assumptions and inconsistencies that may already be causing problems.
Our view
Our view
We think agents are likely to become the main way people use business software for a substantial amount of everyday work. The pace will vary, but the opportunity is clear: people can spend more time deciding, communicating and delivering, while software takes on more of the effort involved in finding information and carrying out routine actions.
Businesses can prepare without predicting which AI provider or protocol will dominate. Well-structured data, accessible applications, documented processes and sensible permissions are useful foundations across a wide range of possible futures.
The organisations that make progress on those foundations now will have more opportunities to put agents to useful work as the technology develops. They will also have better systems for their teams in the meantime.
If you want to understand where agents could help your business, Ostratto can help you review the systems and processes you already have, identify a practical starting point and put the right foundations in place.
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