Tag: LLM agents

  • AI Agents vs. AI Assistants: A Practical Capability Checklist

    AI Agents vs. AI Assistants: A Practical Capability Checklist

    “Assistant” and “agent” are used inconsistently across products. Instead of relying on a label, inspect what the system can initiate, what tools it can call, what state it can retain, and what actions a person must approve. This guide is a capability framework, not a market survey.

    Compare behavior, not branding

    • Initiation: Does the system respond only to a request, or can it run on a schedule or trigger?
    • Tools: Can it search, read, write, send, purchase, or change records?
    • State: Does it retain context between tasks, and can the user inspect or clear that state?
    • Autonomy: Does it suggest a next step or execute it?
    • Oversight: Can a person review the exact action and stop or undo it?

    A feature can combine assistant-like conversation with agent-like tool calls. The useful question is how much authority it has in the workflow.

    Choose the least autonomy that solves the task

    For drafting, a response-only assistant may be enough. For retrieving a status, allow read-only access and verify the source. For making a change, require a person to review the proposed action before it is applied. For irreversible or consequential actions, keep a qualified human decision-maker.

    Example decision

    Suppose a team wants help triaging an incoming request. The system might classify the request and propose a queue. The next step—changing priority, contacting a customer, or closing a case—should be separately authorized. Test ambiguous and out-of-scope requests and define when it must ask for help.

    Risk and governance

    NIST’s AI Risk Management Framework offers a voluntary way to think about context, measurement, and risk management. Microsoft’s agent safety guidance discusses agent-specific safety controls. Neither source makes a product automatically safe; adapt controls to the actual permissions and harm of a failure.

    Describe the capability you actually observe

    Product labels are weak evidence because the same interface can combine a conversational assistant, retrieval, scheduled automation, and tool execution. Describe each capability independently and confirm it in current product documentation or a low-risk sandbox before granting access.

    Question Record Why it changes the boundary
    What starts a run? Person request, schedule, event, or another system Unattended triggers need an owner and a way to pause them
    What can it read? Documents, messages, records, or only the prompt Read access can still expose data beyond the task
    What tools can it call? Read, draft, write, send, delete, or purchase Side effects need least privilege and explicit approval
    What state persists? History, memory, files, and logs, with their owners Retention and deletion must be understood before use
    How is a person involved? Review before action, review after action, or no checkpoint Consequential actions need a meaningful opportunity to stop or correct them

    Example: a help-desk assistant may answer from a document link while an agent can also open a ticket, change its priority, or send a reply. Test the same fictional request in a sandbox and document which actions are proposed versus executed. Start with read-only access; add a write permission only for a task with a named owner, approval, and recovery path.

    This comparison answers “what can the system do?” The separate use-case matrix asks whether a task should be delegated; the workplace control guide explains how to set approvals and rollback.

    Related reading

    For a list of bounded, low-risk tasks, see the agent use-case guide. For human approval, logging, and rollback, read the controlled-use guide.

    About the author

    Femica Maydinda Harend writes practical technology guides for AILooma focused on transparent workflows and limitations.

    Editorial note

    Product definitions and controls vary by version and plan. Inspect current documentation and permissions before enabling tool access.

  • AI Agent Use Cases: A Risk-and-Reversibility Decision Matrix

    AI Agent Use Cases: A Risk-and-Reversibility Decision Matrix

    AI agents are not equally appropriate for every task. This guide helps a reader decide where an agent might assist by looking at repeatability, data sensitivity, impact, reversibility, and available human review. The examples are illustrative categories, not measured deployments or promised productivity gains.

    Use this five-question screen

    1. Is the task repeated with a clear input and expected output?
    2. Can a person verify the result against a source?
    3. Would a mistake be easy to reverse?
    4. Does the task involve personal, confidential, financial, health, or legal information?
    5. Can the system ask for approval before sending or changing anything?

    Illustrative task matrix

    Task pattern Possible support Boundary to keep
    Organizing public meeting notes Suggest headings and action items Human checks names, dates, decisions, and recipients
    Finding an internal policy passage Retrieve likely source text Reviewer opens the cited policy and confirms it is current
    Drafting a routine response Prepare a proposed draft Person checks accuracy, tone, and authorization before sending
    Changing access or approving a payment Explain steps or collect required information Keep the actual approval and irreversible action with an authorized person

    Start with a reversible pilot

    Choose one low-impact task. Define normal, ambiguous, and out-of-scope inputs, then record the expected action and stop condition. Begin with read-only permissions where possible. Review errors, data exposure, and time spent correcting output before considering a broader role.

    When not to automate

    Do not delegate decisions that require accountable professional judgment, informed consent, or a legally authorized person. Do not let an agent send external messages, change records, or make purchases without clear scope and review appropriate to the consequences.

    Apply the matrix to a real task boundary

    Use the matrix to decide how much authority a candidate use case should receive—not to claim that an agent will improve it. Start by writing one task in the form “When approved input arrives, prepare specific output for named reviewer.” If the input, owner, or acceptable result cannot be described, keep the process manual until it can.

    Task pattern Safer starting role Reason to stop or add review
    Find a record in an approved knowledge base Read-only retrieval with source links Permission mismatch, missing citations, or conflicting records
    Classify an incoming request Suggest a category and mark uncertain cases Misclassification changes eligibility, priority, or a customer outcome
    Draft an internal summary Prepare a draft for its owner Sensitive material is outside the approved data path
    Send a message, change a record, or make a purchase Prepare a proposed action; require an explicit approval The action is difficult to reverse or affects someone else

    For a low-risk pilot, record the task owner, permitted data, read/write scopes, approval point, stop phrase or disable control, and manual fallback. Review a sample of normal and ambiguous cases with a person who understands the work. If the workflow creates more correction, access, or exception work than the original process, do not widen its permissions.

    This is a use-case suitability screen. For the separate capability vocabulary, see the agent-versus-assistant checklist; for implementation controls, use the approval and rollback guide.

    Related reading

    For a capability-based distinction between agents and assistants, see the comparison checklist. For workflow design at team level, read the governance-first workflow guide.

    About the author

    Femica Maydinda Harend writes practical technology guides for AILooma, with emphasis on reader-verifiable decisions and clear limits.

    Editorial note

    Examples are illustrative, not evidence of actual deployments. Apply organizational policy and current product documentation to any real workflow.