AILooma

Category: Artificial Intelligence

AI reporting, tools, and practical applications.

  • 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.

  • Use AI for Research Without Losing the Source Trail

    Use AI for Research Without Losing the Source Trail

    AI can help organize a research task, but the evidence still comes from sources a reader can inspect. This guide covers the workflow from question to synthesis. It is distinct from fact-checking one answer: here the goal is to build a source trail before writing a conclusion.

    1. Turn the topic into a researchable question

    Specify the subject, time period, population, and what kind of answer you need. Keep a note of what would change your conclusion.

    2. Use AI only to plan discovery

    Ask for search terms, alternate phrasings, and relevant organizations or document types. Treat those suggestions as leads, not citations. Search for the original document yourself.

    3. Build a source ledger

    For each source, record its publisher, title, date, URL, primary or secondary status, relevant passage, and limitation. Prefer primary materials and government or standards sources where appropriate. Do not cite a source until you have opened it and matched it to the claim.

    4. Compare and preserve disagreement

    Look for different definitions, time frames, populations, and methods. If credible sources disagree, describe the disagreement and what evidence would resolve it. Do not ask a model to smooth away a real conflict.

    5. Draft with traceable claims

    Write one factual claim at a time and attach the source that supports it. Ask AI to organize or clarify your notes, then check that no new claim or citation appeared in the rewrite.

    6. Review the final source trail

    • Does every material fact point to a specific source?
    • Does that source support the exact sentence?
    • Are dates and definitions still current?
    • Are gaps and uncertainty visible?
    • Have sensitive or copyrighted materials been handled under the applicable policy?

    Make the source ledger usable during drafting

    A source ledger is most useful when it captures why a source belongs in the answer, not only its URL. Add fields for the question or claim, source owner, publication or update date, primary/secondary status, exact passage, population or scope, limitation, and the draft sentence it supports. Save a local note or approved reference record so a reader can retrace the decision later.

    1. Search for the organization or document type that would hold the original record, then inspect that source directly.
    2. Capture the exact passage and its surrounding context. A search snippet or an AI-provided citation is only a lead.
    3. Compare definitions, dates, jurisdictions, and populations before treating two sources as contradictory.
    4. Draft from the ledger. Ask an AI tool to organize supplied notes if permitted, then compare the draft line by line with the ledger and remove any claim or citation it introduced.
    5. Leave unresolved points visible. State what the sources establish, what they do not establish, and what additional evidence would change the conclusion.

    Small example: if two official pages give different product-support dates, record the product edition and page update date beside each date. Do not blend them into one broad “Windows support ends” sentence. Check which edition the reader uses and link the applicable current source.

    This article covers research from question through synthesis. For checking one generated answer claim by claim, use the fact-checking workflow; for a team rubric that assigns review intensity, see the risk-tier evaluation framework.

    Related reading

    For checking an individual generated response, see the claim-tracing guide. For evaluating output quality by risk, see the evaluation framework.

    About the author

    Femica Maydinda Harend writes practical technology guides for AILooma focused on workflows readers can verify for themselves.

    Editorial note

    This workflow does not guarantee comprehensive research. Search indexes and source documents change; consult primary sources and qualified experts for high-stakes conclusions.

  • How to Fact-Check an AI-Generated Answer: Trace Each Claim to a Source

    How to Fact-Check an AI-Generated Answer: Trace Each Claim to a Source

    A fluent answer can still contain a false date, a missing qualification, or a citation that does not support the sentence. This guide is for checking one AI-generated answer before you reuse it. It is not a full research workflow or a statistical evaluation of a model.

    Step 1: Separate the answer into claims

    Underline statements that can be checked: names, dates, quantities, quotes, causal explanations, and recommendations presented as fact. Treat each claim separately; one correct paragraph does not validate the rest.

    Step 2: Identify the exact question and scope

    Write down the time period, jurisdiction, product version, population, or definition the answer assumes. Many apparent disagreements come from a changed date or a different meaning of the term.

    Step 3: Find the original source

    Search for a primary source such as a government page, standards body, official documentation, original study, or the company’s release note. Do not rely on a search-results page or a secondary summary when the primary evidence is available.

    Step 4: Compare source and statement

    Check whether the source actually supports the exact wording, denominator, date, and limitation. A survey of respondents is not automatically a statistic about an entire industry. A vendor feature page is not proof of independent performance.

    Step 5: Record a correction

    For each claim, mark supported, partly supported, contradicted, or not verified. Keep the source URL and the reason. Remove or qualify claims that lack evidence before using the answer.

    Step 6: Check material claims twice

    For high-impact or surprising claims, seek an independent authoritative source and ask a qualified reviewer. If sources conflict, preserve the disagreement and explain the date or scope rather than choosing the most convenient number.

    Worked example: verify one fictional date claim

    Suppose a fictional answer says, “The sample application closes on 12 May.” The fictional program page says, “Applications close at 5 p.m. on 13 May.” Record the answer’s claim and the source passage separately. The claim is contradicted on date; the source also adds a time that the answer omitted. Correct the sentence only after checking that the page applies to the same program and year.

    Use a compact claim record:

    • Claim: exact sentence, with any hidden date, geography, version, or population.
    • Source: publisher, page title, URL, update date, and the passage that bears on the claim.
    • Assessment: supported, partly supported, contradicted, or unresolved.
    • Action: retain with context, qualify, correct, remove, or ask a qualified reviewer.

    Do not count two pages that repeat the same press release as independent confirmation. For a disputed or high-impact point, locate the originating record and a genuinely independent authoritative source; if they still disagree, explain the scope difference or leave the point unresolved. Keep an audit note when a correction changes the conclusion, and never silently replace one unsupported exact number with another.

    This is a method example using invented dates, not a real fact-check performed by AILooma. The broader source-led research workflow helps build a source trail before drafting.

    Related reading

    For research that begins with a question and tracks a source ledger, see the AI-assisted research workflow. For a team-wide evaluation rubric, read the risk-tier scorecard.

    About the author

    Femica Maydinda Harend writes practical technology guides for AILooma, emphasizing source checks and transparent limits.

    Editorial note

    This is a verification method, not a claim that AILooma audited a particular model answer. Check current original sources and consult an expert for consequential decisions.

  • How to Use AI Agents at Work with Approval, Logging, and Rollback

    How to Use AI Agents at Work with Approval, Logging, and Rollback

    This guide is for a practitioner evaluating a bounded AI-agent workflow at work. It focuses on permissions and control—not a general list of possible use cases. AILooma has not tested a particular vendor or deployment. Use approved accounts and non-sensitive test material until the system’s data path is understood.

    1. Write the allowed task and prohibited actions

    Describe the input, output, owner, and completion condition. List actions the agent must not take, such as sending a message, changing a production record, or approving a transaction without human review.

    2. Give only necessary permissions

    Begin with read-only scope if possible. Keep credentials in the platform’s approved secret store; do not paste keys into prompts, notes, or logs. Review which documents, accounts, and external services the agent can reach.

    3. Insert a human approval checkpoint

    Before a write, send, or irreversible action, show the proposed change, target, and source evidence to an authorized reviewer. Require explicit approval and record who approved it. If the platform cannot support an appropriate gate, keep the workflow in suggestion-only mode.

    4. Log decisions without retaining excess data

    Keep enough information to diagnose an error: workflow version, tool call, outcome, and approval. Minimize prompt and personal-data retention, restrict log access, and set a retention period. Logging is useful only if the stored data is protected and reviewed.

    5. Test failure and recovery paths

    Use a sandbox or reversible sample task. Test missing input, duplicate events, an unavailable tool, conflicting sources, and a request outside scope. Define how to stop execution, undo a change, and escalate an incident before live use.

    6. Monitor and revisit

    Assign an owner to review failures, permission changes, and vendor updates. NIST’s AI Risk Management Framework and Microsoft’s agent safety guidance are useful starting points, not a substitute for a system-specific threat and impact review.

    Use an action ladder for permissions and approval

    List each connected action before enabling an agent. Start at the least-authority level that can help: read approved material, prepare a draft, propose a change, or execute a change. Keep sending, deleting, publishing, spending, and changing access behind a person’s explicit approval unless an accountable security owner has approved a narrower documented exception. Microsoft’s agent safety guidance also recommends validating tool inputs and gating high-risk operations behind human confirmation.

    Approval should show the action, not just a summary

    Before a reviewer approves a write, show the target record, exact proposed change, source evidence, affected people, and whether the action can be reversed. Do not let a model both generate an action and certify that the action is safe. Record the approver and result using the organization’s approved audit mechanism, while minimizing sensitive prompt or customer data in logs.

    Write the failure and rollback card

    • Duplicate event: pause side effects and reconcile using a stable request identifier.
    • Missing or conflicting source: stop and route to the named owner; do not guess.
    • Unexpected write: disable the integration, revoke its credential if needed, restore the prior value, and notify the owner.
    • Provider or permission change: re-check the data path and approval settings before resuming.

    Keep one documented manual fallback and a person who can activate it. For redesigning a team’s whole process, not just controlling one deployed workflow, see the governance-first workflow guide.

    Related reading

    For a list of candidate tasks and risk boundaries, use the use-case matrix. For a broader prototype lifecycle, see the developer checklist.

    About the author

    Femica Maydinda Harend writes practical technology guides for AILooma focused on understandable workflows and responsible boundaries.

    Editorial note

    This is general information, not a security review or legal advice. Features and controls vary; consult current vendor documentation, your security team, and applicable policies.

  • 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.