AI agents are one of the biggest enterprise topics 2025, but where does adoption stand? In this conversation, KPMG’s Swami Chandrasekaran breaks down the practical realities of implementing agents in large organizations. From the state of enterprise readiness to frameworks like TACO (Taskers, Automators, Collaborators, Orchestrators), this discussion covers what enterprises need to consider before deploying agents at scale.
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Today on the AI Daily Brief a blueprint For Enterprise AI adoption Hello friends Today we once again have a slightly Different type of episode but one that I'm really excited about it is Undeniable that the biggest theme this Year for most Enterprises or at least The most exciting theme to most Enterprises is Agents I have a whole Slew of theories around why I think Agents have businesses thinking Differently even more than perhaps some Gen assistant type tools have but in This conversation I'm joined by Swami Shandra searan the head of the usai Center for excellence for KPMG rather Than just a general overview of agents This conversation comprises a part one Of something of a blueprint for thinking About Enterprise agent adoption or at Least testing Swami shares his Taco Framework thinking about different types Of Agents broken down as taskers Automats collaborators we discussed the Most common challenges that he's seeing Among Enterprises trying to adopt agents And ultimately we try to provide some Positive steps that you can take as an Enterprise to advance your agent Strategy we certainly don't get through An entire blueprint for an agent Strategy we will have to have Swami back To keep going on that as you'll see Swami is definitely not your standard
Consultant he has a deep technology Background working previously as an Executive architect at IBM Watson among Other roles he holds more than 30 Patents and has authored multiple books And articles on applied Ai and so Without any further Ado let's dive into This Conversation all right Swami welcome to The AI Daily Brief how are you doing sir Doing good anel thanks for having me Over big fan of your show appreciate it Yeah we we we were just joking so up Until about I don't know 24 48 hours ago We were talking about the uh the the Hottest Topic in in AI I think for a Very brief moment that's been displaced Maybe by by Deep seek and R1 but broadly Speaking I think uh I think this Conversation about agents still is Pretty pretty down the middle of where a Lot of people are thinking um maybe Before we get into it though I would Love for you to just share a little bit About what you spend your days doing it You know that that gives context for for For this conversation so I live in Dallas I'm a partner at KPMG I lead uh The AI and data labs for the firm and What that actually means is as part of The large transformation program you're Running called aiq that Steve Chase runs Uh the data lab Ai and data Labs is a is A pretty significant part uh the way I
Explain my job to my my 13-year-old is I Do three things um when I say hi it's me And my team we do a lot of Experimentation so for lack of better Word we don't have a full-fledged R&D Function in at the firm so we do a lot Of experiments Innovation R&D around Things that don't exist today but will Exist tomorrow whether it is around how Do we use language models or how do we Break build Advanced rag or knowledge Assistant techniques or even agentic Frameworks or how do we evaluate these Models the second part of what I do is I Help uh establish standardization when It comes to technology Architecture and and patterns for AI Across the FR so we don't do the same Thing five times and the third part of Is given my I'm a history in being in The advisory side of KPMG I work with a Lot of uh folks in the in the in the in Co-incubating new things for our clients So I get closer to clients and Understand problems so I don't get too Disconnected from what I do so nutshell I have think the best job in The Firm um And a lot of uh lot of lot of fun and Lot of responsibilities as well awesome Uh so per perfect setup I think a lot of The conversation today is going to be About the Practical factual kind of Where we are with agents And understanding where you're sitting
Especially relative to clients is is is Useful um let's actually start there With that question when you think about 2025 as relates to agents they are Obviously a key theme they're on Everyone's mind but where are we Actually when it comes to agent adoption Uh particularly in the Enterprise right What what stage are we at uh and let's Start there and there's a lot of Branching questions that I have from There as well yeah let me let me quickly Set the context no pun intended right But to to to get to every agents so when Large language models came out we we Started interacting with it with prompts Ungrounded interactions we loved it and Then we slowly started to bring in more Context through longer prompts PE short Prompting and so forth then thanks to Meta we have this approach with Retrieval augmented generation where we Said that why don't I intercept the Proms and go to a corpus bring back the Relevant chunks and give it to the Model so we got our arms and and uh and Our ourselves wrapped around okay now I Understand the concept of rag or what we Call Knowledge assistants and KPMG but Still with both of these paradigms you Were sitting and typing prompts you were You were away you're doing it you may End up doing Lang chain type chaining And those kind of things but you're
Still typing BRS where is the action um So Agents come Agents the whole the whole concept is Can I can I have these machines go given A larger goal can these machines go Figure Out um and plan and go take uh actions So whether it is researching on a topic Or whether it is um reconciling a Balance sheet against myp systems um you It's now starting to do things so what Fun mentally makes agents are how well You define your instructions your goals Expressed as instructions long form Proms how well those proms are reasoned And Understood into through a planner into Tasks that you have to perform and to Perform the task what tools I need to do The job then there are things like Knowledge memory and uh context and a Whole bunch of things so fundamentally It is giving uh the large language Models not only additional tools but the Ability to do uh reasoning in the Context of a goal or adjacent set of Goals you're trying to Achieve okay Swami you gave a very good Theoretical definition what does it mean Um if you if you now look at what is Possible today um all the things I've Been explaining are possible in a way Using Frameworks like Lan chain and and Lama index and others where you you you
Can Deterministically change those steps for Example if I I want to reconcile a Balance sheet I may have two break Functions each function may have a long Form instruction I take I make that Execution of Function One in Python give That output to section the second Function and I can achieve it there's Nothing truly agentic about it because You are hardcoding the steps the true Agentic behavior is going to be where I Express for example balance sheet Reconciliation what do I do as an expert I say a balance sheet will have these Following Fields I I look for the Following parts in the balance sheet Input then I go to an Erp system and I Do certain things so you are expressing That as a like how a human expert would Express the question now comes can any Large language model even reason and Understand what you're saying probably Till like 6 months ago or maybe little Before that they were not it was very Hard for them uh over every every Iteration of the language models that Came out from all the big Tech the the Reasoning capabilities and more Importantly longer instructions longer Problems they begin to do pretty well Even if you go back to 3 two three years Ago these longer instructions were Impossible to achieve right now you can
Do so what you have the ability is Better reasoning um better understanding Of what you're saying through these long Form instructions that are very critical For agents um that was not possible in The past So what leaves this what what what does That leave with us so you can understand Instructions well you can break them Down into tasks Probably then it now then comes to can You rely can you are those tasks that is That are broken down and the tools that Are used for those tasks are they Reliable enough for you they answer the Jues out there uh the jues out there in Terms of the tools and platforms we've Tried and worked with um it requires it Requires a bit of handhold but the Language models can reason the act of Turning that into a set of tasks a plan Instructions and to go execute um it is Is it's getting there it's getting Better but uh but long story short what We can do today is simple agents I I Have come up with a a simpler definition Or a simple four ways to Define the Types of Agents you can do uh acronymous Taco taskers automator collaborators and Orchestrators which is multi agent Orchestration and and one thing about Taco is people differentiate between uh I've heard people talk about oh certain Agents don't get to access all tools I
My my thing is in the taco framer all All the categories of Agents four types Of agents are going to get access to the Same knowledge Corpus it's going to get Access to the same depth and depth of Tools that the agents would need to Create actions it will have access to Memory it'll have access to the same Alms so all of those f are fixed so what Is different the difference between the Four comes down to planning and Orchestration the the T and taco taskers They singular goals um one goal but can Break down to multiple tasks it can be Chained um easy to manage easy to test Easy to roll out when you go to Automator which is the next they Typically go to cross system cross Application these are end to end Processes order to cash um lead to cash Procure to pay hire to retire they they Touch multiple applications and multiple Systems so the goal may be similar Meaning um ensure streamlined order to Cache process execution but they break Down to S uh sub goals each of the sub Goals May touch different applications In different systems so it gets a bit Complex in terms terms of the scope of What it does planner gets complicated Orchestration gets complicated in the Orchestration you have to manage State And all these things the third part is Collaborators this is where I've been
Pondering over the question so there's This concept of can AI be used as Teamates agents be used as Teamates they're no longer you telling The agent to do something it comes back You you work with it it's like how you Work with your team member on a daily Basis so there is more schiess towards Human Collaboration part partnering with with The machine to get things done uh it's There in the other forms of agent but it Is even more so in this is just Predominantly built that and the last Bit o in the in the taco is uh the Multi-agent where I have agents calling Other agents there is inter agent Collaboration of course the complexity Becomes more with all this so like I Said earlier where are we today I think Um there have been a lot of experiments Prototyping done with the taskers people Have been inherently because there are Quite a few platforms open source Commercial included where you can build Them quickly uh we can talk about that But I think those are in the year of Agents if 25 is I would see more taskers That's my prediction do you Think it's obviously very dangerous to Sort of prescribe one right path without The context of Any Given organization But do you think that that that taco Framework actually is basically are they
Four separate categories only or are They do they have some sort of linear Relationship with one another as you're Thinking about adoption if you're Sitting in an Enterprise where you know It makes sense to start with taskers and Then move to the next or you know H how Do you think about that yeah this is not I don't want this to be a contrived Framework where we retrofit everything Into one of these F uh the framework is Meant for a mental model mental picture Look how can I break down agents not Everything cu the reason for this was Everybody jumped into multi-agent Coordination without even thinking about The basics so that is one second is more Than likely when you go talk to clients They're going to talk about scenarios Which will not only Overlap but will require um Um their focus may be more than likely Starting with okay let me do end to and Off process automation automats CU That's where I need I want to streamline My store Performance Management or I Want to stream line my procure to pay Process or when you go to another client May say look I'm more focused on Augmenting my human potential so give me An AI asent that can act like a teammate For my AP AR Erp Finance kind of proc Domains so yes it is dangerous to put Everything into the bucket but that's
Not the point the point here is to Demystify the whole agentic system and How complexity comes and if you start to Amalgamate and combine that's okay but At least you understood the individual Speed Yeah it's interesting you know I think That one of the things that makes agent Adoption fascinating as compared to for Example uh sort of broader gen adoption Over the last couple years um Enterprises moved very quickly relative To previous you know technology changes To grab onto gen and try to sort of Harness it now obviously there's still Tons of organizations that are behind Feel behind um very few organizations I Think we tend to find that the Organizations who are the farthest ahead Also have the greatest awareness of how Much more they still have to do when it Comes to adoption so it's not like They're they're sort of you know at the End state or anything but I do think That because they've been watching Agents come down the pipeline for a Little while they maybe have a stronger Sense in general of how they want to Eventually use agents the the Possibilities that have them most Excited and I think that it you know it Might be leading to some of what you're Seeing around they're jumping to sort of Exactly what they would like out of an
Ideal state of of what an agent can do They're imagining even ahead of where The technology is rather than sort of Just racing to catch up with what what It can do now uh which which you know Can create challenges just based on you Know what what's actually ready for Prime time and what's not at this exact Moment yeah everybody has a uh an Expectation and a notion of what this Agent should be for them uh if you go Look in the customer service and Marketing function they say my version Of an agent is can I put a can I put a Digital version of a customer For a sof software development or sales Development representative and it can Talk to clients it can negot can ask Questions it can help close a sale and Get paid a commission and come on so They they start thinking it like Synthetic employees you go to into the Enterprise you go into the m and back Office functions they think in terms of Processes there is there is a particular Way in which I I receive review approve Or deny Invoices as part of my larger procure to Pay endend process so I have a Conception of how agent should be in That particular Way it is it is no it is not one siiz Fits all like you said but at the same Time the key responsibility is when you
Go talk about them you're not trying to Take an existing technology and retrofit And say oh I have agents so as an Example one belief I have is good Business process engineering like how You sat and designed business processes For end Processes it took a particular approach Process engineering came out where he Said decompose your domain break them Down into level on through level n could Go up to level seven and 8 where you Kind of have a massive swim Lane view of How your process looks Like that's how we we represented Processes that's not how machines think Now with the reasoning capabilities I Could express that same thing almost Like a long form Instruction and you leave it to the Machine to say look you go to find the Process the steps that are needed to to To execute it so there is also change in How we approach designing the Agents that is also essential and Important Over the outcome is the same I want to a Better efficient leaner process um but You're you're approaching it in a Different way so the point being the Entry point for agents are Different um they're all going to Converge at some point in time but given Where we are in the stage where are we
In it's the expectations are widely Different what do you think about as You're advising clients or even just Thinking about it broadly uh and you're Thinking about agent Readiness in the Enterprise what are the what are the Some of the pillars of consideration how Much is it about data how much is it About policy how much is it about uh Understanding objectives as you've just Articulated you know what what are some Of the key pillars of of agent Readiness Yeah you you kind of gave our three out Of the uh things I was going to say Anyway so first of all why H I start With that Question Um what is the rationale what is the Motivation for so first Define don't go Go to techn technology called agents yet What is the problem you're you're always Trying to solve so if if I'm if I'm a Client if they come in if they they're a Retailer they come and say you know what I want I want better Topline growth Increase in my stores in my bricken Motor stores okay what are you doing Today they say okay I have these things But stores s the sales get affected Because certain stores don't follow Certain kind of policies and procedures Um they don't take into account customer Satisfaction or customer reviews and all Those kind of things okay then we go in
And say okay the goal and objective is To have a a better more tangential Approach to how you do store performance Analysis so you can improve the Performance and increase your top line And view so number one is what I trying To do and and and is Agents even the Right answer so let's assume you've gone Down the path of saying look I want to Optimize My processes reimagine my processes at The same time optimizing my uh Human Resources then you talk about okay where Is the data coming from do you have the Data do you have access to all of the Data have you even first of all Instrumented the data if it is if it has To be digitized um and is that data made Is clean it's all the good things about Data availability and Readiness and Everything the third one is I I don't Think you you mentioned in the in the List Nathaniel which is who is is the Human expert who can articulate what is Happening today and What needs to change How are we going to elicit that Knowledge whether it you pick a domain You pick any any intense domain if it is Some even if it is something as simple As customer service from the point a Customer comes and raises a request for Refund what do you do what is the Process you follow and what is what is The way to reimagine from that point
Onwards using agentic Concepts so human Expertise is still needed to to Articulate I mean there are theories Floating around can I go do simulation Can I look at what humans being do and Learn from from that yeah you can but They're not fully reliable Yet so why agents data human expertise Articulating the the whole thinking Process and the proc and how agents have To be built then getting into U policy Create things okay are there things you Want how much of autonomy you want to Give to these agents it's not a it could Be at a very broad stroke principle Lev Saying look I don't want any decisions That that that that have a financial Implication to be approved without human In the loop maybe I want three steps of Three stages of human in theop so there Is a there is the whole strategy around How do you bring in humans where do you Bring them in where is the level of Oversight what is the kill switch Equalent for agents look like what if You want to stop agents for a day what Is your fall back mechanism in case These don't start to work so all of Those policy trust security reliability Aspects is is one big bucket and the Fourth important bucket is everybody and This is a very opinionated topic I've Seen with clients is how are you going To build agents okay everything fine you
Got the data you got the experts you got Policies you know how to build them Where are you going to go build it so Today there are dime a dozen open source Frameworks the big Tech small Tech Startups they're all they all have their Platform so where do you go standardize And and build again my my my my thought Process there is till this whole thing Settles down you may have to remain Polyl and pick a a few choices be very Opinionated and go build and try them Out and some are going to work some are Not so you have to be ready for Consolidation and merging so where how What is the tool technology Infrastructure that you're going to go To I'm not even using because llms I'm Assuming they're going to get awesome They are awesome already they're going To get continue to get awesome and the Last bit is around skills do you have The skills to build this and um one more Thing after this okay you have the Skills building agents is one thing the Day2 plus operations is a completely Different thing how are you going to Sustain so we going to we we've talked About model drift and data drift now Comes agent drift what's the guarantee The agents are not going to drift it's Going to deviate away from what it was Built for how do you keep them upkeep Them is the data changing
How how how good of a feedback are you Providing back to it for Reinforcement those all come in the day Two plus Operations so top of top of my mind I Think these are these are the kind of Categories of things um I would look at And do you have I think it's a a really Useful framework um how much do you Think people's how much are you seeing People's First experiences being Something that they're kind of rolling Their own you know with one of these General Frameworks versus trying Something that's more offthe shelf I Mean this is kind of only a question for The last few months as more off The-shelf things have been available but You know working with a a customer Service agent or or is this does this Have to do with which category of of Agent to use your framework they're Actually thinking about yeah so if you Double click into where are your Building agents I think it bro it double Clicks into three sub questions or sub Areas are you going to build your own Using open source are you going to pick A commercial platform form like a copal Studio or agent space third option is Are you going to buy the agent so you go To uh agent force is going to say okay I Already have a sales coach agent you're Just going to buy it configure it and
Use it the experience uh is is is Changing by the month what we have today Is not what we had 6 months ago and Again there's another one I the way I Look at the whole agentic tooling spaces There is uh low code tools like Studios And those kind of the word then on the Far right you've got the pro code tools Like the the langars and crew Ai and Autogen of the world then in the middle I call them mid code you can go back and Forth meaning I can write code I can Write and drag and drop so I can do Both initially people tend to go use the Pro code options and they realize while It gives them a lot of flexibility they Have to end up building a lot of things On their So there's a a lot of lines of code to Write and maintain and manage Brittleness starts to kick in unless you Have a well coordinated engineering team Development team you may end up uh Recreating the same thing for example The same tools uh to do the same thing May get recreated right multiple times So there's a there is there is that risk Of having and you need to have a special Set of skills and capabilities to do Coding by yourself now if you come to Look code I mean I could get started Quickly very easily but but I've seen Roadblocks where they say oh I want to Do this
Excel comparison for one of the steps in My agent and I cannot do very deep Excel Analysis because my Exel has multiple Complex cells and rows and head as an Example like I said right that's why the Whole polygard approach is needed like You need to First decide um what is my Agent architecture going to look like What are the tools that I need as of Enterpr price let's go figure out the Strategy to build those tools in a Reusable way and then it doesn't matter If I'm building my agents in my Pro code Or on my loord they all access the same Set of tools so let's focus more on Getting the task done with the same set Of guidelines principles and Safety then and and if you are ready to Keep upkeep these Agents from day to Onwards you make a choice so I think the JW is out there in terms of not one Platform has got everything you need if You have something then is going to be Something it does not give you a a Fiction point you Get this is a uh I don't know if I'll Phrase this question right initially but You know with with Gen right now sort of Non- agentic gen llms and you know Assistant co-pilot style tools a lot of Adoption is happening at least mediated By some central body in the Enterprise That's tasked with thinking about AI Transformation right so maybe it's a
Repurposed Innovation group that touches All the lines of business and all the Back office functions and all the things It sort of just understands everyone's Different stakes and who become the Conduit for different use cases and Different tools and things like that so It's it's topped down not in an Aggressive kind of way but in a uh you Know still still like coming through a Central entity do you think that agentic Adoption is going to mirror that it's Going to come from Central groups Analyzing uh all the different options Or is this going to be a little bit more Bottoms Up where it's a a specific Department or a specific line of Business or a specific area you know Experimenting with something that's Direct and purposeful for them you Cannot stop innovation in the in the Grass roads that's the reality people Are going to keep innovating and come up With new approaches because the the role I'm in I belong to that Central Organization so so fair disclosure right I'm I'm providing my perspective with That sitting in in that part in that That side of the word I believe helping Standardize on the approach The technology the platforms including Safety that you incorporate when you're Building the agents will go a long way In helping folks in the Departments and
Different business units spend their Time and energy in building Agents where I see a lot of time and Energy being spent is trying to build Your own agentic platform or trying to Trying to make your own agentic platform This is like saying I'm I'm trying to Build my own uh I'm trying to build a Car but I have four groups in the in the Company and each one of them is building Their own supply chain or the assembly Line why why why even try that why don't We build one good efficient Model T Toyota Tesla you pick the best supply Chain assembly line uh including the Supply chain that powers it and you Focus on designing the model 3 or the Toyota Camry or the whatever whatever Your favorite card is so standardizing Giving them the platform and providing The guidelines and let them bring the Focus on the hard part the hard part Like I was telling earlier eliciting Knowledge of everyday work and Translating that into an agent that Takes time that significant piece of Work so who's going to do that if Everybody's focusing on I'll also build The platform and I'll also build the Agent so it sounds like a bit of a both End there's going to be functions that Are relevant for kind an org wide or at Least cross function discussion uh from An infrastructure perspective in
Particular while there's also a clear Kind of purpose for what the you know Individual units or groups are going to Actually need and understand yeah yeah And one one other observation data point Is we're already finding the individual Groups heavily time constrained meaning They don't have a lot of time to go to Um R&D pick a platform evaluate a Platform evaluate choices what kind of Evaluations do I do on agents this Versus another they already kind of they Already have like things to go ship and Build so trying to take this as uh those As as much as away and have the central Group help provide that Guidance let me go down even a level From uh from that sort of department or Functional or or group Level how much are you thinking about You know individual level employee level Adoption and the challenges therein Either when it comes to you know getting Employee perspective perspectives on Which tasks are actually suited for Automation or which things they you know They'd like to have agentic support for Um as well as you know a question of Employee attitudes and concerns around You know Replacements and and things Like that how much are you seeing that Enter the the discussion as as companies Are are moving into this space so the uh The one in one side there are tools so
For example KPMG is R out um Microsoft M365 co-pilot to all of our employees in The US For example except for federal um so They have access to all of the tools the Ability to create what are called Personal co-pilots where you can point It to your own SharePoint Corpus and Start to interact with it right so they Can pretty much do this in a matter of a Few seconds today so there is that level Of capabilities that are made available By big Tech like Microsoft and made Available for large corporations the Reality is they are made available they Are there the next evolution in that is They're also going to say okay you can Build your own agents to automate your Daily tasks so there's one Theory from The big Tech where they want to push Their tools for more adoption better Adoption but they're saying look you can Build assistance agents on your own and It's going to be easy my take is look Well that is all good on paper but Imagine you're going to have hundreds And thousands of these agents all over The place the kind of actions the agents Are going to take we have to carefully Manage them right you don't want it to Do start doing um things that will leak Leak your IP leak your knowledge leak Your data put you at risk and be whatnot So one tool is people who are Builders
The Builders of Agents will have to be Certain types of people who have gone Through not only Skilling training and Other kinds of things but also Understand the implications of building Agents in a particular way so you're Going to you're going to start seeing Personal agents that is confined to only What I do as work so today in my Computer I could have a shell script That can do things that is confined to What is happening in my own specific Environment Enterprise grade agents I Think will'll take a path where it will Be built by um folks who have gone Through a certain level of pedigree and Steps if I could say that I don't think Either of them are going to stop do you See a convergence of those at some point Where companies start you know I mean One of the the fascinating things about Gen in general is that the it's the First time that shadow it has been while Yes a concern also uh an area for Innovation that they're actively trying To understand so they can potentially Bring in right like you want to Understand what people are using their Personal Gils for to sign up for not Only because you want them to not put Important company data on those Platforms without without your knowledge But also because you might want to adopt Those um and given how much of a race
There is to the personal assistant side Of of Agents right we're recording this Just a few days after operator has come Out I I can see there being a a sort of A blend where Enterprises start trying To adopt Agents from a top down kind of Way or at least sort of you know a unit By unit Group by group function by Function kind of way and employees are Bringing in assistants that have sort of Started to automate their own personal Processes at the same time yeah since The bir of operators let's take that as An example I could build when when Operators may come available for Everybody I could build an operator that I could use for my for example my Weekend planning or my my calendar Assuming I can log into outlook on the Web look at my calendar and uh see Overlapping meetings and come and tell Me which ones I should consider Cancelling as an example but that's me H Having unleashing an operator building An unleashing an operator is happening In my personal environment space Assuming 10 other people find about it And say that's a very good use of Operator as a very good personal agent Can you share that with me so the point I'm trying to make is the personal Agents the scope of sharing is going to Be limited um if you keep it that way It's not permeating across the
Enterprise it's still being built on Approv this is not like somebody gone Rogue and built their own agent on an Unapproved platform I'm still talking About approved platforms but built Personally but the the scope of sharing Is limited I foresee a world where You're going to see organic Innovation Happening and somebody's going to crack The the the nut on um look oh this is The most Innovative use of operators or Or agents or Copart Studio B whatnot That I think should be made available at The Enterprise level to go through that Level you got to go through stage Gates Of testing evaluation safety and other Things so you have proper governance in Place cuz for the Enterprise I I see Them no different than treating them as Products they're rolling out products in Your Enterprise you're not just going to Roll out things randomly on the fly Without kny water destroying and Inter So I think the I I had I had some idea Coming into this what I wanted to do but What's become clear is that I think this Episode will kind of stand and I'm going To frame it as uh almost sort of like a An agent Readiness checklist but I think We just did part one um what I would Suggest is uh I maybe one wrap-up Question but then we should come back And do this you know maybe maybe next Month uh and do a part two where we get
Into maybe some more specifics around Use cases and things like that um I Guess until get there if you had one General piece of advice for the next Month you're not going to get to talk to To these listeners as they're thinking About adopting agents in their companies What's one one thing you would encourage Them avoiding or trying or you know just You know setting setting as part of Their framework uh to kind of maximize How how they how they how they think About adopting enterpr uh agents in in This year yeah one thing is always hard Uh but let me try um one thing I'll Highly encourage is um don't don't stop Experimenting I mean you you have to do That only then you would understand what Is what is right or wrong but one thing I would highly encourage everybody to go Do is talk to your respective um Transformation technology AI leaders First question to ask is the things I've Been talking about what are we going to Do about if I have the next best agentic Idea where do I go build it where do I Build it in a way that it is not Throwaway work because that could be a Rallying point for many things meaning What are the agents what kind of agents Are they how do I build those agents What data do I need to build the agents Because I've seen everybody talk about Thinking about agents talking about
Agents debating about them but when it Comes to rubber hits the road of I need The data or I need to go build them it Becomes analysis paralysis so we are in The mode if if we are very if you all of Us believe this is the year of Agents Then you should have already picked a Platform if you've not highly and Courage go think about where do you go Build and then everything else would Follow what are you ready are you are You are you do you have the skills to go Build it what else do you need to think About they'll all naturally fall awesome Well like I said I I really do think This should be a part one uh and we Should come back again but I appreciate You spending some time with us today I Think it's you know everyone is trying To wrap their head around this Particular question right now so uh Invaluable to have you here to to talk Through it thank you Nat happy to come Back