Mayur Macwan writes about hiring and recruiting, with a focus on how AI can help screen applicants while people retain hiring decisions. The posts also describe building RoundOne AI, a tool for evaluating applicants against a role specification.
Mayur Macwan writes on hiring, recruiting, and AI screening, arguing people keep hiring decisions.
Best for
A recruiting-tech startup commissioning argument-led thought leadership on AI in hiring.
Not for
Consumer skincare or video-led campaigns that need on-camera demonstration.
Key takeaways
Give AI agents a controlled door instead of blocking them.
One permission no AI agent should get in hiring.
The first CVs read set the standard for every CV after.
Check first: Confirm booking details, rates, audience analytics, and video samples before contacting them.
Main ideas
Give AI agents a controlled door instead of blocking them · One permission no AI agent should get in hiring · The first CVs read set the standard for every CV after
Mayur Macwan writes about hiring, recruiting, and AI's role in applicant screening.
Their LinkedIn posts connect practical hiring problems—such as early applications shaping a recruiter's standards and a strong candidate falling just outside a shortlist—to their work building RoundOne AI.
They also explore how AI agents might participate in hiring while arguing that people should retain the hiring decision.
Their short, example-led arguments link specific incidents or technology developments to broader points about hiring.
Mayur Macwan makes abstract concerns about screening concrete through hiring stories.
They describe how the first CVs can anchor later judgments, then contrast a ranked shortlist with the overlooked candidate who became RoundOne's first employee.
That case qualifies their product argument: the system flagged the candidate, but the hiring story turns on signals—such as what the person had run and how they discussed the business—that a form could not capture.
In posts about AI agents, they use a retail login protocol to explain how agents might help candidates while leaving the final hiring decision to a person.
Across these examples, they pair a clear claim with a practical tension rather than presenting AI as a complete substitute for human judgment.
Mayur Macwan writes about hiring and recruiting, and two arguments run through the posts.
Read more
First, AI agents will arrive in hiring as they are arriving in retail, so the question is not whether to allow them but what they are permitted to do; the hiring decision stays human.
Second, ordinary screening fails: the first applications a reviewer reads anchor everything after them, and a ranked shortlist rewards what is easy to justify rather than who is best.
Macwan ties both arguments to RoundOne AI, a screening product that evaluates applicants against the role specification, and uses the company's own hiring round as the public test case.
Give AI agents a controlled door instead of blocking them
Macwan argues that companies fighting AI agents treat paying customers as intruders.
Read more
Courts and partner platforms have already treated an agent acting on a user's instruction as the user. The better move is to set the terms the agent operates under: who gets in, and what it may do.
Amazon sued Perplexity over an agent that shopped for customers, then blocked Meta's Muse agent with a pop-up calling it an "unauthorised AI agent".
Tencent took the other route, opening WeChat to selected agents on its own terms.
Meta, with Sierra, Walmart, Shopify and Stripe, published the Personal Agent Protocol: the agent arrives as a guest and can look around; once the customer signs in, they choose read-only or write access.
Macwan's summary of the choice is "Lock it, or own it."
Mapping the agent-login model onto recruiting, Macwan separates what agents can carry from what they cannot.
Read more
Volume, matching and the first conversation are fair game; the decision is not. They state the boundary as a rule, not a preference.
In Macwan's version of the hiring channel, a candidate's agent browses as a guest, finds the role, reads the spec and checks fit.
Once the candidate signs in, it can apply, take the first screen and arrive with the candidate's actual history rather than a fabricated one.
Macwan refuses to grant it "write access to the decision", so whether someone gets the job stays with a person.
The line: "AI screens, humans decide.
The line never moves."
Macwan notes that a fortnight earlier, a second channel for agents sounded far-fetched. Now the plumbing exists in another industry, with hiring rarely staying far behind retail.
The first CVs read set the standard for every CV after
Macwan describes anchoring bias in screening from their own experience as a hiring manager.
Read more
The first few applications calibrate what "good" feels like, and later candidates are measured against that early frame rather than the role. They stress that this is not a recruiter's fault, but how a brain handles volume.
The first application in one pile had an Ivy League degree, strong writing and clear numbers; everything after it was measured against the accident of who applied first.
Applications one to ten, or thirty, get full attention; by the hundred-and-fiftieth, a reviewer is pattern-matching and skimming in seconds.
Macwan says this is one reason they built RoundOne AI, so every applicant is judged against the role spec and "#212 gets the same read as #1".
A shortlist ranks who was easiest to justify, not who is best
Macwan argues that a ranked shortlist reflects what a process could defend on paper, while the strongest signal often has no box on an application form.
Read more
Their response is to keep reading past the ranking and look at candidates a scoring system flags without selecting.
Hiring RoundOne's first employee through the company's own product, Macwan got a ranked ten they would have been happy to interview, plus one separate outlier: fewer years than the role asked for, but short internships where people reported to her, and a way of talking about the business rather than the job in a seven-minute screening call.
Macwan hired her as employee number one; she is still with the company, doing more than the job she was hired for.
The conclusion: "Your shortlist is a ranking of who was easiest to justify, not a ranking of who's best."
The story leaves a tension in place: the role-spec evaluation Macwan argues for produced the ranked ten, while a separate outlier flag caught the hire. They treat the outlier signal, not the ranking, as the part worth acting on.
Answers in one short, concrete argument—not a general statement of enthusiasm—how Macwan thinks AI should enter hiring.
Read more
Mayur Macwan maps a development in retail onto hiring.
Walmart, Shopify and Stripe are among the first partners on the Personal Agent Protocol, a login standard for AI agents published by Sierra and Meta.
In the design they describe, an agent arrives as a guest and can look around; once the customer signs in, they choose what it may do: read-only or write.
Macwan’s translation: a candidate’s agent could find the role, read the spec, check fit and apply, arriving with the candidate’s actual history rather than a fabrication.
The decision itself is the boundary they want to protect.
“In hiring, there's one permission no agent should get: write access to the decision.”
They close on “AI screens, humans decide.
The line never moves.”
For brands planning a campaign
A usable structure for ghostwritten thought leadership: borrow a live analogy from another industry, then draw the boundary the brand wants to hold. The read-only-or-write framing and the “AI screens, humans decide” line are quotable and can anchor a closing recommendation.
Shows the argument Macwan returns to across posts—whether platforms block AI agents or set the terms they operate under—and the reasoning behind their hiring position.
Read more
Macwan sets two responses side by side.
Amazon sued Perplexity over an AI agent shopping for customers; Tencent opened WeChat to selected agents on its own terms, keeping the keys.
They add a detail they say they left out earlier: in August the Ninth Circuit overturned Amazon’s ban, reasoning that when an agent acts on a user’s instruction, it is the user accessing the site.
Amazon blocked the door anyway—when people sent Meta’s agent, Muse, to shop there, a pop-up called it an “unauthorised AI agent”.
Meta then published the Personal Agent Protocol with Sierra, Walmart, Shopify and Stripe.
Macwan’s summary is “Lock it, or own it.”
For brands planning a campaign
A ready-made “block versus own” contrast frame that a recruiting-tech brand could reuse for its own agent policy, with the court ruling as supporting context. Before citing the ruling, ask for the case and date to be confirmed against primary court records.
The clearest statement of the problem RoundOne AI exists to solve, drawn from Macwan’s experience reading applications as a hiring manager.
Read more
Drawing on their own experience closing a role with recruiters, Macwan describes anchoring bias in screening.
The first application in the pile had an Ivy League degree, strong writing and clear numbers, and everything after it was measured against the accident of who applied first.
CVs one to ten (or thirty) get full attention and calibrate what “good” feels like; by the hundred-and-fiftieth, a recruiter is pattern-matching against that frame and skimming in seconds.
Macwan does not frame this as a recruiter’s fault, calling it how the human brain handles volume, and connects it directly to building RoundOne AI so every applicant is evaluated against the role spec.
Their claim is that applicant #212 gets the same read as #1.
For brands planning a campaign
Anchoring bias is a concrete, checkable hook for a piece on screening at volume. The first-ten-CVs example can become a scenario, an interview question or a testable claim a brand is willing to be measured against.
A first-person case showing what Macwan means by signals application forms have no box for, and how they used the product they built on their own hire.
Read more
When hiring RoundOne’s first employee, Macwan ran the process on the company’s own product and got back a clean ranked ten.
Separately, the system flagged one more candidate as an outlier: fewer years than the role asked for, but everything else present—short internships, things that person had actually run with people reporting to them, and the way they talked about the business rather than the role, all inside a seven-minute screening call.
None of that fits an application form or a sortable column.
Macwan hired the outlier, who is still with the company and doing more than the job they were hired for.
Their conclusion: “Your shortlist is a ranking of who was easiest to justify, not a ranking of who's best.”
For brands planning a campaign
A human story that balances the product argument, with a named counterexample—a shortlist that missed the company’s own first hire—that a commissioned piece could build around when arguing for wider screening criteria.
Mayur Macwan frames AI agents as an infrastructure question for hiring, not just a tooling question.
Read more
They contrast Amazon's legal fight against Perplexity's shopping agent with Tencent's decision to let selected agents into WeChat on its own terms, summarizing the choice as "lock it, or own it." They map the Personal Agent Protocol—a shared agent-login standard from Sierra and Meta, with Walmart, Shopify, and Stripe as early partners—onto recruiting: as a guest, a candidate's agent can find a role and check fit; once the candidate signs in, it can apply, take a first screen, and bring the candidate's history into the conversation.
Uses current industry news—agent protocols, court and retail developments—as a setup, then translates it into a hiring scenario.
For brands planning a campaign
For a thought-leadership piece on AI agents in hiring, Mayur offers an analogy-led frame: an agent becomes another channel, while the platform or employer sets the access rules. A brand can use this to discuss agent access, candidate representation, and recruiting workflows without treating agents as simple job-board bots.
Mayur describes the experience as a hiring manager: the first few CVs set the standard for every later application.
Read more
An early Ivy League application with strong writing and clear numbers calibrated their sense of "good," and by CV #150 they were skimming against that frame. They say this is how the brain handles volume and one reason they built RoundOne AI, which evaluates every applicant against the role spec so "#212 gets the same read as #1."
A first-person hiring anecdote paired with a product claim; uses a numbered application sequence and explains cognitive bias plainly.
For brands planning a campaign
This gives a campaign concrete evidence for an argument about screening consistency, first-impression bias, and role-spec-based evaluation. A brand can use the first-CV anecdote as a recognizable opening before moving to structured evaluation and what consistent review is meant to change.
Mayur tells how RoundOne's own first hire did not make the product's ranked top ten.
Read more
The product returned a defensible ranked ten, then separately flagged an outlier: a candidate without the requested years but with short internships where people reported to her, and a way of talking about the business rather than the role in a seven-minute screening call. They hired her as employee number one and write that a shortlist can rank the people who were easiest to justify, not the people who are best.
A product-run hiring story with a concrete exception turns an internal shortlist result into a public argument.
The post describes one outlier from one hiring round and does not present it as a general hiring rule.
For brands planning a campaign
For a recruiting-tech brand, this is a ready-made case for "keep reading past the ranking" and for evaluation signals that application forms do not capture. It supports content about outliers, structured screening, and the limits of sortable criteria.
Mayur draws a boundary around agent permissions in hiring: agents can carry volume, matching, and the first conversation, but "there's one permission no agent should get: write access to the decision."
Read more
The line they repeat is "AI screens, humans decide. The line never moves."
Uses the read/write permission model from the agent-protocol analogy to argue for a boundary around human decisions.
For brands planning a campaign
A thought-leadership commission can use this as an ethical spine: describe what AI can automate in recruiting and where human judgment remains accountable. It allows a brand to discuss AI in hiring without presenting automated hiring decisions as the goal.
Two posts connect Mayur's arguments to RoundOne AI.
Read more
They say the product evaluates applicants against the role spec so later applicants get the same read as early ones.
They describe running the first hire through the product, getting a ranked ten, and finding a separate outlier.
The posts use the product as a test case for claims about screening and shortlists, not as a feature announcement.
First-person build-in-public storytelling pairs product behavior with the hiring decision it influenced.
For brands planning a campaign
For a brief seeking a founder-operator voice, Mayur can speak from specific product and hiring experience. A campaign should use the product as evidence for the argument, not as a standalone ad.
Mayur Macwan's posts identify RoundOne AI as a hiring tool they built and run, and describe using it to fill the company's first role.
Read more
Four LinkedIn posts analyze how AI agents get access to shopping and hiring platforms, naming Amazon, Perplexity, Tencent, Meta, Walmart, Shopify, Stripe and Sierra as subjects. The posts do not identify any of those companies as sponsors, affiliates, clients or partners.
RoundOne AI
Owned business
Macwan says they built RoundOne AI, a product that evaluates job applicants against a role specification rather than the first CV in the pile.
Read more
In a separate post, they describe using it to hire the company's first employee: an outlier candidate the product flagged outside the ranked ten, who was still with the team at the time of writing.
Mayur Macwan writes about hiring and recruiting technology, usually through one concrete story at a time.
Read more
Their posts on AI agents in hiring argue that platforms and employers should set the terms agents operate under rather than block them, and that people should make the hiring decision.
Their screening posts make the case that the first applications a reviewer reads set the standard for everything that follows, while ranked shortlists reward what is easy to justify over who is best.
Macwan tests both claims against RoundOne AI, a screening product they write about building and running on their own hires.
Readers interested in recruitment services rather than tooling will find more in the creators listed below.
Jain is a recruiting-technology founder who writes about AI in hiring, his product's evolution and shifts across the recruiting stack.
Like Macwan, he uses his company as evidence for an argument rather than as an announcement.
Both write for people evaluating how hiring work gets done.
Jain's posts centre on sourcing, candidate databases and funding-driven moves across recruiting tech; Macwan's focus on agent access rules, screening bias and keeping hiring decisions human, with product claims grounded in named anecdotes.
Baxi is a recruitment-industry voice on LinkedIn who also writes about AI in hiring and the practical problems recruitment businesses face.
Read more
They often open with a direct question to owners and draw on first-person observation. Their register and audience overlap closely with Macwan's.
Baxi writes about running and fixing recruitment agencies, covering business development, tool sprawl, AI planning and the specialist network RecXperts. Macwan focuses on hiring technology, screening design and agent protocols.
Georgiou is a recruitment founder who writes on LinkedIn about building a firm around quality over quantity and the hiring experience from both sides.
Read more
Like Macwan, they turn firsthand hiring experience into an argument about how the work should be done.
Georgiou writes about running a specialist headhunting business, covering sectors, client relationships and service quality. Macwan focuses on AI, screening tools and agent permissions rather than search delivery.
Mayur Macwan’s posts suit a thought-leadership brief about AI in hiring: they argue for role-based applicant screening, highlight how early applications can anchor human reviewers, and draw a parallel between AI-agent permissions in retail and hiring.
Read more
Before commissioning, request recent LinkedIn audience analytics for geography and other relevant demographics; for employer, occupation, or seniority, ask for a documented audience survey or comparable evidence.
Mayur Macwan’s four full LinkedIn posts cover recruiting, applicant screening, AI agents in hiring, and industry news about AI agents’ access to retail platforms.
Read more
They do not describe partnership formats, sponsor policies, rates, deliverables, approval processes, disclosure examples, or an official inquiry route.
Amazon, Perplexity, Tencent, Meta, Walmart, Shopify, Stripe, and Sierra appear as subjects of industry news, not as commercial partners.
Before planning a campaign, ask for an official media kit or partnership contact.
A recruiting-tech startup commissions a thought-leadership piece on how AI agents should enter hiring: what they may do, what stays with a human, and how the company's position differs from the loudest voices in the market.
Reasons
Macwan already writes the exact argument a campaign about human judgment in AI hiring needs, in publishable form. One post takes a live development in another industry — a shared login standard for AI agents, with Walmart, Shopify and Stripe among the first partners — and maps it onto hiring: as a guest, a candidate's agent finds the role and checks the spec; once the candidate signs in, it can apply, take the first screen and arrive with real history rather than a fabrication. They close with a ready-made thesis: agents can carry volume, matching and the first conversation, but 'there's one permission no agent should get: write access to the decision.'
The companion post supplies the industry frame a thought-leadership piece needs: Amazon sued an agent-based shopper and kept blocking agents anyway, while Tencent opened WeChat to selected agents on its own terms. Their summary — lock it, or own it — gives a brand a contrast it can adopt rather than a slogan it has to invent.
Macwan writes from inside the problem. The anchoring-bias post is the clearest: as a hiring manager, they describe the first application in the pile setting the standard for every CV after it, with early applications calibrating what 'good' feels like and reviewers pattern-matching by CV #150. That is a specific, checkable observation rather than a general statement about bias.
Macwan has a product case, not just an opinion. RoundOne's own first hire did not make the product's ranked top ten; the tool flagged the candidate separately as an outlier, and they hired them. That story — a shortlist ranks who was easiest to justify, not who is best — can carry a piece that would otherwise be abstract.
Conditions
The commission must be driven by opinion and argument, not a product walkthrough. Each post uses RoundOne as the test case for a claim; none reads as a feature announcement. A brief that turns it into one loses the voice that makes the material work.
No partnership format, rate, deliverable, approval process or official inquiry route is published. Get booking details in a direct conversation or media kit.
When to choose a specialist
Decline for this creator.
Read more
Brief
A consumer skincare brand commissions short-form video: a demonstration of a new serum used in a morning routine, with before-and-after close-ups and a spoken verdict for paid social placement.
Reasons
The available posts are four written LinkedIn posts; no video, spoken segment, transcript or short-form clip is available to assess. Before booking a campaign that depends on demonstration, on-camera delivery or visual before-and-after, ask for relevant video samples.
The posts connect Macwan to recruiting, applicant screening, hiring decisions and AI agent access to platforms—not to consumer product categories. Amazon, Walmart, Shopify, Stripe, Meta, Sierra and Tencent appear as subjects of industry news, not as evidence of a commercial relationship.
There is no documented sponsorship or product-placement format to build on: no rate information, no deliverables, no disclosure examples, no advertised booking path.
The audience section provides no dated audience-composition data. Before booking, ask for it to check whether Macwan's readers match a skincare buyer.
Reasons not to skip fit entirely
A B2B opinion brief with no production requirement could still work; this recommendation applies to the video-dependent consumer brief, not to every possible commission.
A worked campaign example
Angle
Build the piece around a single borrowed idea: AI agents are arriving in hiring the way they arrived in retail, and the question brands should answer first is not whether to let them in but which permissions to grant. The argument lands on a boundary: agents may carry volume, matching and the first conversation; the hiring decision stays with a person.
Headline
The permission your hiring agent should never get
Sample arguments
Read or write is a useful way to describe hiring permissions, because it makes the boundary something a hiring team can name in a policy rather than an instinct.
Blocking agents and setting their terms are different strategies with different costs; the platforms that opened a door with rules kept control, and the one that locked it is still fighting arrivals at the door.
Consistent screening is the fixable half of the first-CV problem: the same applicant gets the same read whether they arrived first or 212th.
A shortlist is a ranking of who was easiest to justify. It is not a ranking of who is best, and the difference shows up in candidates who arrive through paths the form does not have a field for.
Structure
Open with the parallel, briefly. An agent can now arrive as a guest, look around, and act only once the account holder signs in and chooses read-only or write. State the hiring version in the first two paragraphs rather than saving it for the end: guest access means finding and checking a role; signed-in access means applying and taking a first screen; the decision is not on the list.
Turn to what goes wrong without that boundary. Use the first-CV observation: the first few applications read as the standard for every one after, and by the hundredth the reviewer is matching against a frame built by whoever happened to apply first. Name the consequence for candidates rather than recruiters, since Macwan frames this as how the brain handles volume, not as a recruiter's fault.
Name the outlier problem. In Macwan's own hiring round, the ranked shortlist excluded the person they eventually hired because the missing signal — short internships running real work, and how someone talks about the business rather than the role — had no column on the form. This is the argument for evaluating against the role spec rather than the pile order.
Close on the boundary. Agents can hold the volume, the matching and the first conversation; a human holds the decision. Give the reader one concrete next step: write down which steps in their own process an agent may take and which one is reserved, and hold the reserved one.
Verification before publication
Confirm the legal and industry details from primary sources. The Ninth Circuit reasoning, the Amazon and Perplexity dispute, and the Personal Agent Protocol partners are reported in Mayur Macwan's posts; cite the court record and the standard's own documentation for these claims.
The two RoundOne hiring stories are first-person accounts without supporting documentation in the posts. Use them as Macwan's own experience, attributed rather than presented as verified case studies.
The posts have no dates, so avoid framing any of this as breaking news or as the start of a trend with a stated timeline.
Confirm commercial terms, deliverable format and disclosure expectations directly before contracting; none are published in the posts.
Why this works
The brief asked for thought leadership on AI agents in hiring, and Macwan's material already contains the thesis, analogy, counterexample and closing line. The work here is editing and verification, not generating a position from scratch.
Choose Kalpesh Baxi for a campaign aimed at recruitment agency owners about what to fix before AI changes the economics of their business.
Read more
Baxi starts from a task-level argument: AI will not replace a team; it replaces tasks.
In a recruitment consultant's week, sourcing, screening, scheduling, chasing and CRM updates are repeatable work, while understanding people and closing deals remain human.
Baxi also offers a competitive frame for a thought-leadership piece: the real threat is a business down the road with the same headcount and far more capacity because it worked out the difference between tasks and jobs first.
The practical hook—list the tasks, not the roles—is a ready-made line a brand can build a structure around.
The tradeoff is specificity.
Baxi's posts are advisory and owner-facing.
They do not offer an AI-agent access standard, a read-or-write permission model or a named product case like RoundOne's own first hire.
The second post asks what recruitment business owners would fix and introduces RecXperts, a network of vetted recruitment specialists; it does not address agent boundaries in hiring.
A piece commissioned from Baxi would be stronger on agency operations than on the concrete mechanics of how agents enter hiring.
If the brand wants a policy-shaped argument about what an agent may do and what stays human, Mayur Macwan's permission framing is the closer match.
If it wants an owner-level reorganisation argument, Baxi is the better choice.
Choose Vas Georgiou for a campaign seeking a headhunter's firsthand account of why human judgement is the product.
Read more
Georgiou's posts describe quitting an HR job in 2020 to build Heilsa after being frustrated by recruiters who sent irrelevant CVs, disappeared after a rejection and treated hires as transactions.
Six years on, Heilsa has placed people across 30+ countries and narrowed its focus to Law, Cybersecurity & Tech, Financial Services and Healthcare.
The strongest support for a thought-leadership piece is the client review quoted in the second post: understanding the company, team and hiring needs not just on paper, but in practice.
Georgiou ties that directly to having worked in HR rather than heard about it.
For a campaign about what remains human in hiring, that is a more grounded testimonial than a product-feature argument.
The tradeoff is distance from the AI-agent question.
Georgiou's posts do not discuss agents, platform permissions, screening automation or a product that coordinates with AI.
A piece built from these posts would argue for specialist, quality-over-quantity headhunting and the value of having done the hiring job; those arguments would need to be translated into a campaign about AI agents in hiring.
Georgiou's posts are also older, from April 2026, than Mayur Macwan's recent agent-access posts, giving a brand less direct insight into current commentary on agents entering hiring.
Choose Georgiou when the desired tone is an experienced human counterweight and sector credibility.
Choose Macwan when the core is agent permissions and screening design.
Choose Mehul Jain for a campaign arguing that AI agents are becoming a full-stack part of recruiting, with funding and product examples.
Read more
Jain's first post makes a practical sourcing argument: recruiters hiring for niche roles often wait for a mythical candidate with the exact skills, who fits the budget, is ready to move, applies and is also a culture fit.
For ObjectARX, Fortran + HPC or a specific ERP implementation, the best people are not in the applicant inbox.
Searching the old database, looking for adjacent experience, mapping the twenty companies where the skill exists, then reaching out by email, WhatsApp, phone and LinkedIn are all steps an AI agent could automate.
Jain ties that workflow directly to Fabric searching existing candidate databases, ranking people and starting outreach after approval.
The second post offers a market trend for a thought-leadership piece: Metaview raising $110M after moving from interview notes to an AI recruiting coworker, Jack & Jill's $40M Series A for candidate and hiring-team agents, and Juicebox's $116M for sourcing and outreach agents, with Fabric itself expanding from AI interviews and resume screening toward a full-stack hiring platform.
The tradeoff is emphasis and commercial framing.
Jain's material is about adoption, funding and workflow coverage, not the ethical boundary for agent decision rights.
Macwan draws a line, saying agents may carry volume, matching and the first conversation but not have write access to the decision.
Jain's posts, by contrast, point toward agents taking entire chunks of recruiter work off the plate and ask what the recruiting stack will look like in two years.
Jain also writes as someone building Fabric, so a commissioned piece risks reading as product-adjacent market commentary unless the argument stays focused on buyer decisions and is independently verified.
Choose Jain for a market-landscape piece on AI in recruiting workflows.
Choose Macwan for a boundary-setting piece on what agents should and should not decide.
Four LinkedIn posts by Mayur Macwan cover AI agents in retail and hiring, the anchoring effect of the first CVs read, the limits of ranked shortlists, and RoundOne AI as the case behind the arguments.
The posts do not show publication dates, so nothing here is dated or presented as a trend timeline.
No long-form articles, talks, interviews, video, media kit or dated audience data are mentioned in the posts.
Open gaps: any earlier or signature work beyond RoundOne AI, current posting dates, and any documented partnership or commercial terms, none of which appear in the posts.
