Artificial Intelligence as a Gateway to Agents and Software

Artificial intelligence as a gateway to agents and software is the idea that employees should not need to navigate ten separate applications to find information or begin a business task. An AI assistant can interpret a request, obtain permitted information and, where justified and safe, connect the user to specialised software or a narrowly authorised agent.

This was the central idea of Positive's article published on 19 March 2025, authored by Miljan Radanović. The original described the assistant as a coordinating layer for a network of “small digital helpers” and business applications, with modular construction compared to LEGO bricks. This restored edition keeps that vision while drawing clearer lines between an assistant, an agent, a business data source and authority to execute an action.

What does an AI gateway to business software actually mean?

A gateway provides a natural-language entry point. An employee might ask: “Which sales opportunities need follow-up?”, “Where is the current policy?” or “Prepare a support ticket”. Different systems behind that interface may include CRM, project management, knowledge repositories, service desks and analytics.

The assistant interprets the user's request. An integration layer verifies identity and permissions. The business application remains the authoritative source for records and executed changes. This separation helps prevent a language model from becoming an unrestricted administrator of company systems.

A conversational interface must distinguish a suggestion from a completed action. Users should know which source supported the answer and whether anything was changed in the underlying software.

Assistants, agents and conventional automation

An assistant usually explains, searches, summarises or drafts. An agent can be assigned a limited goal and access to tools across several steps. Conventional workflow automation follows predefined rules and triggers.

For example, an assistant can explain how support requests are handled. An agent may collect a description and prepare a ticket within its granted scope. A standard workflow can then notify the assigned technician. Each stage needs ownership, records of what happened and rules for human approval.

Not every task benefits from agentic behaviour. A predictable process may be cheaper and easier to validate through traditional automation. AI adds value when interpreting varied requests, but that does not justify unrestricted autonomy.

Why modular design matters

The original Positive essay used LEGO bricks as a metaphor: each tool, agent or information source has a distinct purpose and can be added or replaced without rebuilding the entire system. In practice, knowledge search, CRM access and ticket handling do not have to live in one large application.

Modularity supports gradual adoption. A company can begin with a reliable internal knowledge assistant, add a limited service-desk integration and later test more complex sales workflows. It is easier to isolate faults and improve components when responsibilities are separated.

However, modularity also requires consistent integration contracts, access checks and descriptions of available actions. The existence of an API does not mean that every connected application should share all its information.

A common data lake does not imply universal access

The original concept described a central knowledge base or data lake. Such infrastructure can gather diverse information, but it should not be treated as an unrestricted pool available to every assistant and employee.

Public documents, internal procedures, commercial agreements, customer data and confidential records need different access policies. Permissions must still apply when AI retrieves information. A convincing natural-language question should not reveal a contract the employee is not entitled to see.

Retrieval-augmented generation (RAG) can search approved information and provide relevant passages as context for a model. This can make maintaining current knowledge easier, but it does not guarantee that every interpretation is correct. Important responses should allow source verification.

Example: connecting a request to CRM

An employee asks: “Which opportunities should we contact this week?” The assistant identifies a CRM request, while the integration verifies the person's identity and access rights. The CRM returns only permitted records.

The assistant may summarise priorities and draft proposed next steps. If creating tasks is allowed, a controlled business service performs the action, potentially after confirmation, and records the result. The user must be able to see the difference between a draft and an actual update.

This is why “connecting AI to company data” is not a complete implementation plan. CRM records need to be accurate, access boundaries need to be respected and information owners need a way to correct errors.

Example: internal IT support

Another employee says: “I cannot access a shared folder”. The assistant can ask basic questions, retrieve approved troubleshooting guidance and determine whether a ticket is needed. If the issue persists, it prepares or submits a service request in the proper category.

That does not mean an AI model should freely change account permissions. Access resets and infrastructure changes need authorised processes and appropriate approvals, even when the chat interface makes those actions technically easy.

Useful measurements include accurate classification, time to an appropriate response and the quality of human handoff. A large number of chats does not mean that incidents were resolved. Our related article, An AI Chatbot as Part of Business Operations, explores this distinction.

Six foundations of the original Positive approach

1. Stable IT infrastructure. Systems must be available, monitored and maintained. AI cannot compensate for an unreliable business application behind the interface.

2. A culture willing to learn. Employees need practical training, realistic expectations and a way to identify and report incorrect outputs.

3. Clearly defined processes. Before granting an agent authority, define the workflow, its start and end conditions, approvals and responsibility for failures.

4. Suitable tools and integrations. Use existing business capabilities where practical, and favour components that can be maintained or replaced.

5. Strong security. Manage identity, permissions, secrets and logs. Each component needs only the access necessary for its function.

6. Accurate information. A persuasive AI answer cannot make outdated or incorrect source records reliable. Data needs ownership, quality checks and maintenance.

Together these foundations matter more than the choice of a fashionable model.

Security and accountability across multiple agents

Connected agents can create chains of action, where an incorrect output becomes the next component's input. A further risk is prompt injection: an attacker places instructions in a message or document to influence an AI-powered workflow.

OWASP documents threats including prompt injection, sensitive information exposure and excessive permissions in language-model applications. For that reason, identity, action scope and data access must be checked by trusted software rather than left to the model's own statements.

Practical controls include narrow permissions per tool, confirmation before consequential changes, an effective stop mechanism and audit records identifying the initiator, information used and actual actions. NIST's AI Risk Management Framework offers a broader governance approach.

Building the architecture incrementally

First, select one process: perhaps approved knowledge search or support-request preparation. Inventory the relevant records and verify their quality.

Second, connect one system with minimal permissions. Test missing records, denied access and conflicting sources rather than only the happy path.

Third, test ambiguous requests, adversarial instructions and unavailable services. Define when the assistant should admit uncertainty and pass the task to an employee.

Fourth, measure correct outcomes, elapsed time, handoff quality and the cost of keeping information and integrations current.

Fifth, add another component only after the initial workflow is dependable. This preserves flexibility and avoids automating too many poorly understood processes at once.

What happens if a company does not adopt an AI gateway?

The original author asked whether companies that ignored AI risked falling behind. Today the more useful answer avoids fear-based marketing: not every business needs a central AI gateway immediately.

An organised CRM, better documentation or ordinary automation may be more valuable for a company whose basic processes are still inconsistent. Another organisation may genuinely benefit from a coordinated AI interface across several established systems.

The difference should be determined by a controlled test of business value, costs and risks, rather than model popularity or competitive anxiety.

People remain responsible

A core idea of the original article was that AI should help people rather than serve as a pretext for excluding them. Understanding customers, judging consequences, communicating with colleagues and accepting accountability require appropriate human oversight.

Routine steps may be highly automated, but someone in the organisation must still own the policy, source information, outcome quality and response to errors.

A useful system should create more room for high-value human work. That claim should be tested rather than assumed, accounting for any hidden work introduced by supervision and maintenance.

From the historical PAM idea to today's Positive ecosystem

In 2025, Positive described connecting AI assistants with its business solution PAM. This matters as historical context, but it should not be presented as proof that every capability described at the time is available in today's product offering.

Within the present Positive ecosystem, modular integration can be assessed through ONE business software, specialised AI services from Cybercompany and infrastructure and security from CoreTech. Actual connections depend on each product's supported functions and the organisation's technical requirements.

For related context, see How AI Agents Change Business and Cybercompany in the Positive Ecosystem.

Conclusion: one simple interface, many controlled possibilities

The vision of AI as a gateway is not merely a way to chat with software. It is a way to coordinate trustworthy information, appropriate tools and accountable people around business tasks.

Modular design supports incremental delivery. Stable infrastructure, a learning culture, defined processes, suitable integration, strong security and accurate data make the result sustainable. The best starting point is a single business task whose outcome can be verified, not a promise that every system will run itself.