Speaker 1: Welcome to the sentient Code, where intelligence is engineered, autonomy
Speaker 1: is emerging, and a line between human and machine grows thinner.
Speaker 1: Each episode, we decode the algorithms, explore the robotics, and
Speaker 1: examine the ideas shaping the future of artificial minds.
Speaker 2: Picture this. You open your laptop, you fire up your browser,
Speaker 2: and you pull up your preferred AI interface. Right and
Speaker 2: there it is the little textbox staring back at you,
Speaker 2: just waiting exactly, the blinking cursor. It is waiting for you,
Speaker 2: waiting for you to set the context to engineer the
Speaker 2: perfect prompt. The guide it step by step through.
Speaker 3: Task, and it is phenomenally capable. But until you hit enter,
Speaker 3: it's essentially frozen in time.
Speaker 2: Yeah, it's a stateless entity completely. But now what if
Speaker 2: you never had to type that initial prompt again? What
Speaker 2: if the AI was already running, maintaining state and making
Speaker 2: decisions in the background while you were fast asleep.
Speaker 3: I mean, it completely flips the dynamic. We are conditioned
Speaker 3: to think of these models as highly advanced conversational calculators.
Speaker 4: You know, input, output and done exactly.
Speaker 3: You provide an input, you get an output, and then
Speaker 3: the system just goes dormant, but shifting from a reactive
Speaker 3: paradigm to a proactive, stateful one, it changes the entire
Speaker 3: foundation of human computer interaction.
Speaker 4: It's a huge leap, it really is.
Speaker 3: It moves the AI from a tool you wield to
Speaker 3: a colleague you manage.
Speaker 2: And that is the massive shift we are unpacking today.
Speaker 2: We're looking into Anthropics, highly anticipated project that's currently sitting
Speaker 2: in internal testing as of early April twenty twenty.
Speaker 3: Six, right code named Conway.
Speaker 2: Or Claude Conway. Yeah, yeah, And the mission here is
Speaker 2: to understand how we are moving from passive chat interfaces
Speaker 2: to always on, persistent digital coworkers.
Speaker 3: Which is a fundamental redesign of the technology.
Speaker 2: It is because honestly, giving software the autonomy to act
Speaker 2: without my constant supervision, well it sounds incredibly powerful, but
Speaker 2: also like a fantastic way to rack up a mass
Speaker 2: of aws bill Oh definitely, or you know, trigger a
Speaker 2: PR disaster if it just goes completely off the rails.
Speaker 3: Both are incredibly valid concerns and I think they speak
Speaker 3: to why this transition is so complex. To really grasp
Speaker 3: why Conway is generating such intense internal buzz at Anthropic,
Speaker 3: we have to look at the boundaries of our current architecture.
Speaker 2: The limitations of what we have right now.
Speaker 3: Exactly right now. Even with powerful models, the session is ephemeral.
Speaker 3: When you close the browser tab, the context window just drops.
Speaker 2: It forgets everything.
Speaker 4: Right.
Speaker 3: The system doesn't remember what you discussed yesterday unless you
Speaker 3: manually feed that data back into a new session. So
Speaker 3: the bottleneck isn't the intelligence or the reasoning capabilities of
Speaker 3: the model. The bottleneck is amnesia amnesia exactly. It requires
Speaker 3: constant human initiation just to maintain momentum.
Speaker 2: Okay, let's unpack this because I think the best way
Speaker 2: to visualize the difference is to look at organizational structures. Okay,
Speaker 2: I like that right now. Interacting with an LM is
Speaker 2: like having access to the world's most brilliant reference librarian. Sure,
Speaker 2: but to get any value out of them, you have
Speaker 2: to physically walk up to the reference desk, articulate a
Speaker 2: highly specific query, wait for them to fetch the materials,
Speaker 2: and then you have to synthesize it yourself.
Speaker 3: And if you want to follow up, you're walking right
Speaker 3: back to the.
Speaker 2: Desk exactly Conway. On the other hand, sounds like hiring
Speaker 2: a dedicated chief of staff. Yes, someone who has their
Speaker 2: own desk, who knows your ongoing priorities, and who initiates
Speaker 2: the research before you even realize you need it.
Speaker 3: That maps perfectly onto the architectural shift here. Conway operates
Speaker 3: essentially as an AI operating system.
Speaker 2: An operating system, right.
Speaker 3: It's built around the claud For family of models. But
Speaker 3: it is an environment rather than just a chat in
Speaker 3: our face. The engine driving this autonomous chief of Staff
Speaker 3: relies on an incredible scale of memory.
Speaker 2: How big are we talking.
Speaker 3: We are looking at a context window capable of handling
Speaker 3: upwards of one million tokens.
Speaker 2: Wait a million, Just to put that into perspective for
Speaker 2: a second. A million tokens is roughly equivalent to holding
Speaker 2: the entire Harry Potter series plus the Lord of the
Speaker 2: Rings trilogy in active working memory simultaneously. Right.
Speaker 3: Roughly, Yes, it's massive, and that massive context is what
Speaker 3: enables this long horizon reasoning. Okay, but the key mechanism
Speaker 3: here isn't just holding a lot of text. It's how
Speaker 3: Conway handles persistence without constantly retraining its neural network weights.
Speaker 2: Because retraining constantly would be computationally.
Speaker 3: Impossible, exactly, It would cost a fortune and take way
Speaker 3: too long, So instead Conway uses that massive context for
Speaker 3: advanced in context learning. It maintains a continuous running.
Speaker 2: Log like an internal scratch pad.
Speaker 3: Yes, exactly like a scratch pad, across hours, days, or
Speaker 3: even weeks. Before it goes into a dormant state, it
Speaker 3: summarizes its current state and writes it to its memory.
Speaker 3: Oh wow, And when it wakes up, it reads its
Speaker 3: own journal. It remembers the actions it took yesterday, and crucially,
Speaker 3: it learns from the outcomes.
Speaker 2: So if an API call failed on Tuesday, it's.
Speaker 3: State vector reflects that and it will automatically attempt an
Speaker 3: alternative routing on Wednesday.
Speaker 2: Which is brilliant, but it naturally brings up a huge
Speaker 2: logistical hurdle, right, because if this system is acting as
Speaker 2: my chief of staff running twenty four to seven in
Speaker 2: the background, it needs to interact with the digital world.
Speaker 2: And my immediate reaction is wait, if I am not
Speaker 2: hitting enter to trigger a prompt, how does it know
Speaker 2: when to wake up?
Speaker 3: That's the big question.
Speaker 2: Yeah, and AI just randomly executing tasks in the background
Speaker 2: sounds like pure chaos.
Speaker 3: What's fascinating here is how they have architected the environmental awareness.
Speaker 3: It doesn't rely on a constant, expensive polling loop.
Speaker 2: Where the AI is awake twenty four to seven asking
Speaker 2: should I do something now? Should do something now?
Speaker 3: Right? That would be incredibly inefficient. Instead, it operates entirely
Speaker 3: on a ven driven triggers. Okay, think of it like
Speaker 3: setting up tripwires across your digital ecosystem. Conway stays at
Speaker 3: a highly efficient, dormant state of passive monitoring until an
Speaker 3: external event physically wakes it up and hands it an objective.
Speaker 2: So a tripwire would be something like a VIP client
Speaker 2: sending an email with the word urgent, or maybe a
Speaker 2: database flag getting flipped.
Speaker 3: Yes, exactly does it.
Speaker 2: Hook directly into those systems to listen for that?
Speaker 3: It integrates deeply into your workflow. It could be a
Speaker 3: new pull request opening on GitHub, a sudden calendar alteration,
Speaker 3: or a massive spike and user traffic on your server.
Speaker 2: Got it.
Speaker 3: When that specific programmatic condition is met, the system receives
Speaker 3: a payload, Conway activates, reads the new information against its
Speaker 3: persistent journal, and then executes the pre defined action strategy.
Speaker 2: But hold on, if we connect this to the bigger
Speaker 2: picture of enterprise security, that actually sounds terrifying.
Speaker 3: I'm sure it departments are sweating.
Speaker 2: Because if Conway is just waiting for a signal from
Speaker 2: the Internet to wake up and start executing complex autonomous tasks,
Speaker 2: couldn't a bad actor just spoof an email or fake
Speaker 2: a server request. They could effectively hijack my a I
Speaker 2: co worker by sending it a malicious trigger.
Speaker 3: You're hitting on the core vulnerability of any event driven architecture,
Speaker 3: and it's exactly what Anthropic had to engineer around. Conway
Speaker 3: relies on highly secure webhooks to listen for these triggers, okay,
Speaker 3: and it enforces strict cryptographics signature verification. Specifically, it utilizes
Speaker 3: x hub signature two hundred and fifty six headers for
Speaker 3: all incoming payloads.
Speaker 2: Okay, x hubs signature two fifty six. So it's acting
Speaker 2: like a cryptographic bouncer at the door.
Speaker 3: That's a good way to look at it.
Speaker 2: And I'm assuming this isn't just checking a basic password.
Speaker 3: Far from it. It's an unbreakable mathematical seal. When a
Speaker 3: signal comes in. Let's say your inventory database sends a
Speaker 3: webhook saying stock for item A is zero, right, that
Speaker 3: payload is hashed by the sender using a complex algorithm
Speaker 3: and a secret key that only your server and Conway share.
Speaker 2: Okay, so they both had the key, right.
Speaker 3: The sender attaches that hash to the message. When Conway
Speaker 3: receives the payload, it performs the exact same mathematical hashing
Speaker 3: process on.
Speaker 4: The data, and if they don't match, If.
Speaker 3: The resulting hash doesn't perfectly match the one attached to
Speaker 3: the message, Conway drops the request immediately.
Speaker 2: It doesn't even read it.
Speaker 3: It won't even wake up the language model to read
Speaker 3: the prompt. It ensures the agent only ever responds to authenticated,
Speaker 3: untampered sources.
Speaker 2: Okay, so the cryptographic bouncer lets the trusted signal through.
Speaker 2: Conway wakes up, it reads its journal, and now it
Speaker 2: has to actually do the work. Yes, and from what
Speaker 2: I understand, it doesn't just quietly use APIs behind the scenes.
Speaker 2: It can actually browse the visual web. It can which,
Speaker 2: as someone who currently uses APR automations to run my life,
Speaker 2: I swear if a vendor changes a single pixel on
Speaker 2: their website or renames a CSS class, my entire automated
Speaker 2: workflow shatters into a million pieces.
Speaker 3: And that fragility is exactly what Conway is designed to
Speaker 3: bypass Conway features native browser automation, but it doesn't rely
Speaker 3: on brittle dom scraping, where it just blindly looks for
Speaker 3: a specific line of code on a page. Instead, it
Speaker 3: uses compute to visually parse the layout of a site,
Speaker 3: much like a human does. Oh wow, So if a
Speaker 3: competitor radically updates their pricing page, throwing off all your
Speaker 3: static webscrapers, Conway can navigate to the new url, visually
Speaker 3: identify the pricing tables regardless of the underlying code changes,
Speaker 3: extract the new data, compare it to your internal metrics,
Speaker 3: and draft a strategic response.
Speaker 2: So it's literally acting like a human analyst clicking around
Speaker 2: on Chrome, adapting to visual changes on the fly.
Speaker 3: Yes, and to exponentially scale that capability, developers can build
Speaker 3: custom extensions specifically for.
Speaker 4: Conway extensions, like browser extensions similar concepts.
Speaker 3: These are packaged in a proprietary format dot CNW, dot.
Speaker 2: Zip, CW dot zip. Okay, so we are looking at
Speaker 2: an ecosystem purpose built for an aiagent rather than a
Speaker 2: human user.
Speaker 3: That is the intended architecture. Just as browser extensions give
Speaker 3: you custom UI tools or block ads. These dot CNW
Speaker 3: dot zip files allow enterprise developers to build d native
Speaker 3: integrations into Conway's ecosystem, so.
Speaker 2: I could build one for my specific workflow.
Speaker 3: Exactly, you could install an extension that grants Conway highly specific,
Speaker 3: authenticated access to your proprietary HR software or maybe your
Speaker 3: AWS back end. It creates a standardized way to give
Speaker 3: the AI new skills without having to rebuild the entire
Speaker 3: agent from scratch.
Speaker 2: Here's where it gets really interesting, because Anthropic didn't just
Speaker 2: wake up one morning and decide to build a persistent browser, controlling,
Speaker 2: cryptographically secure AI out of thin air.
Speaker 4: No, this has been a long time coming, right.
Speaker 2: You can trace the development of this over the last year.
Speaker 2: Building an ecosystem where an AI can safely use custom
Speaker 2: extensions means the AI first had to learn how to
Speaker 2: interact with computer systems at a base level. Yes, we
Speaker 2: saw this start with claud code, which was very terminal
Speaker 2: based manipulating files for developers. The major pivot point was
Speaker 2: the transition to claud Cowork earlier this year.
Speaker 3: The January twenty twenty six research preview of claud Cowork
Speaker 3: was an absolutely vital stepping stone. Yeah, it was their
Speaker 3: first real foray into an agentic environment designed for general
Speaker 3: knowledge workers rather than just software engineering.
Speaker 2: Cowork was impressive. You could give it a high level
Speaker 2: goal like take these five raw data exports, clean them up,
Speaker 2: and build me a quarterly review presentation.
Speaker 4: And it would do it.
Speaker 2: It would navigate your files and build the deck. Yeah,
Speaker 2: but it still felt constrained. It felt like handing a
Speaker 2: project to a brilliant intern who legally doesn't have the
Speaker 2: authority to sign the checks.
Speaker 3: That's a great analogy.
Speaker 2: Like it could do the prep work, but it couldn't
Speaker 2: finalize anything.
Speaker 3: But that limitation was by design. Cowork was intensely gold driven,
Speaker 3: but the architecture heavily enforced human in the loop oversight.
Speaker 2: Right.
Speaker 3: The system could do the heavy lifting of data synthesis,
Speaker 3: but the consequential actions, sending the final emails, committing code
Speaker 3: to a production environment, executing a financial transfer, those remained
Speaker 3: gated behind user approval.
Speaker 2: So you were still the bottleneck.
Speaker 3: You are the supervise acting as the final security checkpoint.
Speaker 2: But Conway drops that requirement. It upgrades the intern to
Speaker 2: full corporate signing authority.
Speaker 3: It does, which is a massive leap in trust. Conway
Speaker 3: takes the baseline capabilities of cowork and embeds them into
Speaker 3: this continuous, persistent loop we've been discussing, right, But to
Speaker 3: do that safely, to give it that signing authority, and
Speaker 3: PROPIC had to build an internal regulatory system. They utilize
Speaker 3: what is known as managed agents infrastructure.
Speaker 2: Okay, wait, so it's not just one massive AI brain
Speaker 2: handling the execution and the oversight simultaneously.
Speaker 3: No. Relying on a single model to police itself during
Speaker 3: a complex, multi day task is incredibly risky. Managed agents
Speaker 3: infrastructure involves deploying supervisor agents.
Speaker 1: Ok.
Speaker 3: These are specialized, highly efficient models whose sole function is
Speaker 3: to audit the primary worker agent.
Speaker 2: Like an internal affairs department.
Speaker 3: Exactly as Conway executes a task, say researching competitors and
Speaker 3: updating your database. The supervisor agent runs parallel inference just
Speaker 3: watching it. It constantly evaluates Conway's state vector and scratchpad
Speaker 3: to ensure the worker isn't caught in an infinite loop,
Speaker 3: that it isn't hallucinating data, and that it isn't violating
Speaker 3: its core system.
Speaker 4: Constraints, and what happens if it does.
Speaker 3: If the supervisor detects an anomaly, it can issue a
Speaker 3: system level halt command to the worker agent and Conway
Speaker 3: orchestrates this entire hierarchy autonomously.
Speaker 2: Okay, so we have the architectural foundation, we have the
Speaker 2: million token memory acting as a persistent journal, the cryptographic
Speaker 2: webhooks waking it up, the visual browser control adapting to changes,
Speaker 2: and the supervisor agent's acting as an internal audit team.
Speaker 3: That's the full package.
Speaker 2: Let's bring this down to earth, right to the listener's desktop.
Speaker 2: What does this actually look like in practice? If I
Speaker 2: deploy Conway as my digital chief of staff, how does
Speaker 2: that fundamentally change my Tuesday workflow?
Speaker 3: Let's apply it to a real world business intelligence scenario. Okay, perfect,
Speaker 3: Imagine your company's global supply chain data every night at midnight.
Speaker 3: In a traditional setup, a human analyst logs in at
Speaker 3: nine zero am, spots a strange anomaly in the European numbers,
Speaker 3: spends three hours cross referencing shipping logs, and finally presents
Speaker 3: a preliminary report after lunch.
Speaker 2: Right, half a day is gone just identifying the scope
Speaker 2: of the problem exactly.
Speaker 3: But with an event driven.
Speaker 2: Setup, the midnight database sink is the trigger.
Speaker 3: Yes, at twelve zero one am, Conway wakes up and
Speaker 3: reads the new data. Its supervisor of agents ensure it
Speaker 3: stays on task. It detects a fifteen percent drop in
Speaker 3: European fulfillment speeds.
Speaker 2: And because it has browser control.
Speaker 3: It autonomously opens its native browser scans regional European news outlets,
Speaker 3: identifies a localized wildcat strike at a major shipping port,
Speaker 3: cross references that with competitor inventory levels, and calculates the
Speaker 3: projected impact on your Q three margin.
Speaker 2: While I'm sleeping.
Speaker 3: By three point am, it has synthesized the root cause,
Speaker 3: drafted a comprehensive mitigation strategy, and pushed a Slack message
Speaker 3: to the executive channel. When you wake up and pour
Speaker 3: your car. The crisis hasn't just been identified, the strategic
Speaker 3: analysis has already finished.
Speaker 2: That level of leverage is unbelievable. It completely eclipses static
Speaker 2: automation platforms. We are moving from if X happens, trigger
Speaker 2: why to if X happens, figure out why, understand the context,
Speaker 2: and execute the best possible solution.
Speaker 3: It replaces rigid logic with dynamic judgment, and.
Speaker 2: That dynamic judgment is the key to scaling complex operations,
Speaker 2: but it is also the source of the most significant risk.
Speaker 4: Yes, it is right, I have.
Speaker 2: To play Devil's advocate here because listening to this, I
Speaker 2: can't help but think of the Sorcerer's apprentice.
Speaker 3: Oh that's a good comparison.
Speaker 2: You know, Mickey Mouse in Chance the Broom to carry
Speaker 2: the water falls asleep and wakes up drowning because the
Speaker 2: automated worker lacked the contextual judgment to know when the
Speaker 2: job was actually done. When you grant an AI system
Speaker 2: unsupervised autonomy over days or weeks, what happens when a
Speaker 2: micro error occurs on day one? Doesn't the probability of
Speaker 2: failure approach one hundred percent over a long enough timeline.
Speaker 3: This raises an incredibly important question, and frankly, it is
Speaker 3: the primary reason Conway remains an internal testing. The reality
Speaker 3: check on autonomous agents is severe. The first critical vulnerability
Speaker 3: is exactly what you're pointing to, reliability and the mathematics
Speaker 3: of compounding hallucinations.
Speaker 2: Because if it's acting on its own journal entries, a
Speaker 2: hallucination becomes a false memory that it treats as fact.
Speaker 3: Yes, in a long horizon execution, an AI might make
Speaker 3: a minor incorrect assumption during hour two of a seventy
Speaker 3: two hour workflow. Let's say an agent has a ninety
Speaker 3: nine percent success rate per individual reasoning step.
Speaker 4: That sounds excellent it does.
Speaker 3: But over a sequence of one hundred autonomous steps, that
Speaker 3: one percent error rate compounds, resulting in roughly a thirty
Speaker 3: six percent chance of task failure. Wow, by our forty
Speaker 3: that tiny initial assumption has completely derailed the workflow, and
Speaker 3: anthropics internal research highlights a fascinating psychological hazard here, the
Speaker 3: autonomy paradox.
Speaker 2: Let me guess as the system proves it can handle
Speaker 2: the work humans completely.
Speaker 3: Check out precisely the issue. As Conway demonstrates competence, users
Speaker 3: grant it more independence and check the audit logs less frequently.
Speaker 2: It is very similar to the self driving car problem.
Speaker 3: Exactly, you trust the autopilot so implicitly that you stop
Speaker 3: watching the road, which is exactly the moment you need
Speaker 3: to intervene.
Speaker 2: Right.
Speaker 3: The data shows that even with supervisor agents, edge case
Speaker 3: interruptions where the system requires human clarification are still common.
Speaker 3: If the human has grown complacent, the system either stalls
Speaker 3: indefinitely or worse confidently, hallucinates.
Speaker 2: A path forward, which leads to the second massive reality check.
Speaker 2: Privacy and control a huge issue because if Conway is
Speaker 2: going to act as a chief of staff and draft
Speaker 2: that supply chain report at three point zero am. It
Speaker 2: needs access to a terrifying amount of data. It needs
Speaker 2: everything deep access to my local files, my Gmail, my
Speaker 2: company's Google Drive, are internal Slack channels, my calendar, basically
Speaker 2: my entire digital brain.
Speaker 3: And giving an autonomous agent that level of lateral access
Speaker 3: demands intense encryption and airtight compartmentalization. Yeah, enterprise IT departments
Speaker 3: are going to require mathematical certainty that Conway won't accidentally
Speaker 3: email a draft of upcoming layoffs to the entire company
Speaker 3: while trying to you know, optimize.
Speaker 4: Your HR folders right.
Speaker 2: That would be a nightmare.
Speaker 3: The audit trails for these actions have to be flawless
Speaker 3: and instantly reviewable.
Speaker 2: And if you combine that deep access with the dot
Speaker 2: CNW dot zip extensions we talked about earlier, the security
Speaker 2: implications are wild. We're opening up a massive new attack.
Speaker 3: Surface without question, even with x hub signature cryptographic signing
Speaker 3: on the triggers. Anytime you allow third party extensions to
Speaker 3: dictate internal actions, you introduce severe risk.
Speaker 2: Because someone else wrote that code.
Speaker 3: A poorly coded or intentionally malicious dot CNW dot zip
Speaker 3: extension could act as a trojan horse, granting an attacker
Speaker 3: backdoor access to the agent's memory or its authenticated API keys. WOW,
Speaker 3: prize grade governance, strict extensions, sandboxing, and continuous monitoring are
Speaker 3: going to be absolute prerequisites before a system like Conways's
Speaker 3: wide commercial deployment.
Speaker 2: So what does this all mean for us? Looking at
Speaker 2: the trajectory from stateless prompts to this stateful persistent architecture,
Speaker 2: it is clear we are standing on the edge of
Speaker 2: a completely new era. We really are the age of
Speaker 2: the passive interface. The blanking cursor waiting patiently for our
Speaker 2: instructions is officially fading. We are transitioning into an era
Speaker 2: where AI isn't just a tool we pick up and
Speaker 2: put down.
Speaker 3: Right, it's becoming a continuous presence.
Speaker 2: We're entering the age of proactive, persistent companions, systems that
Speaker 2: act like peers, that manage their own workflows, and that
Speaker 2: keep the lights on long after we have clocked out.
Speaker 3: It fundamentally redefines the concept of digital leverage. You are
Speaker 3: no longer just augmenting your personal typing speed or your
Speaker 3: individual research capacity. You are essentially managing an artificial workforce
Speaker 3: that can scale indefinitely. The technical mitigations, the supervisor age,
Speaker 3: the cryptographic security, the persistent context loops that they're all
Speaker 3: falling into place.
Speaker 2: But the societal and organizational impacts are entirely uncharted completely. Yeah,
Speaker 2: the technology is one thing, but how our legal and
Speaker 2: social structures adapt to it is a completely different.
Speaker 3: Puzzle, and that leaves us with a critical unresolved tension
Speaker 3: to consider as this technology moves toward public release. When
Speaker 3: your AI agent is operating with full corporate signing authority,
Speaker 3: making autonomous decisions in the background, the lines of accountability
Speaker 3: become dangerously blurred.
Speaker 2: That's a scary thought.
Speaker 3: If your AI employee, acting on a broad directive to
Speaker 3: optimize your operational costs, inadvertently violates a vendor's terms of service,
Speaker 3: scrapes a protected database, or accidentally commits corporate fraud. Who
Speaker 3: is legally liable? Do the authorities hold the developer accountable
Speaker 3: for a failure in the supervisor agents, or do they
Speaker 3: hold you, the human manager, responsible for the actions of
Speaker 3: a piece of software living on your hard drive. Where
Speaker 3: does the liability of the tool end and the liability
Speaker 3: of the user begin