Agentic AI: Dissecting the Future of AI Workflows | Memra's Founder Amir Behbehani

**Amir Behbehani** (0:00)
The work of an AI engineer is really to kind of build those systems that effectively build systems. I guess if you're building agentic frameworks and then deploying those agents to effectively write code, and then the code is for the purposes of building an application, then you're building the systems that effectively build the constituent systems that build the application. And in that regard, it's sort of more like industrial engineering than it is necessarily software engineering.

**Conor Bronsdon** (0:29)
13% of all pull requests are bot-created today, and they are creating a unique impact on your SDLC. Linear B's upcoming research exposes the effects bots are having on your team's developer experience and productivity, and engineering orgs who created a system for managing bot-generated PRs are able to reduce their entire review load by over 6%, while also making drastic improvements in their security and compliance posture. You want to learn how your team can manage bot-generated PRs and get early access to Linear B's data report? Head to the show notes to register for our upcoming workshop on September 24th or 25th. Welcome back to Dev Interrupted, everyone. I'm your host, Conor Bronsdon, and today I'm joined by Amir Behbehani, a mathematician and AI ML expert. Amir is both a founder and the chief AI engineer at Memra. He's had previous exits to both Google and Meta. Amir, thank you so much for joining me today.

**Amir Behbehani** (1:17)
Thank you for having me.

**Conor Bronsdon** (1:18)
Yeah, I'm really excited for this conversation because we've talked a lot about how AI is impacting software engineering from the perspective of engineering leaders. But we haven't really had the opportunity to dive in-depth on what's happening as far as the forefront of it and kind of what we see moving forward with someone who's a deep expert in the research side. So in today's conversation, we're going to have that chance to zero into the future of AI and software engineering and hear from you about kind of the cutting edge of research. Plus talk about agentic AI and how that may transform software workflows or other workflows in the world. But before we jump in, I do want to remind our listeners, I know we say it every time, but it really does matter. If you enjoy this episode, please just take one moment to rate and review the Dev Interrupted Podcast on your app of choice. Stitcher, Google Podcasts, Spotify, whichever it is, it helps us bring more insightful conversations with leaders like Amir. And if you really love it, tweet it or zed it. Post on LinkedIn, we love to hear from you. But with no further ado, Amir, let's dive in. Agentic AI is rapidly redefining how we interact with technology right now.
Can you start by explaining for our audience what Agentic AI means, just to make sure we're all on the same page, and how it differs from prior concepts folks may have heard of, such as RPA or robotic process automation?

**Amir Behbehani** (2:35)
Sure. I think of Agentic AI as blocks of code that have reasoning capability. So they actually have access to an LLM. That's the first thing. Then they have access to long-term memory. And you can think of that as akin to the rag layer. So that would be the vector databases plus the graph databases plus other long-term forms of memory. Then they have access to a short-term memory that increases their adaptability. And then they have access to tasks on which they're trained. And then finally they have access to some sort of integration layer. They're integrating to enterprise applications or other workflows. And in so doing, unlike, let's say, RPA, because I often get the question, how does this technology contrast with RPA? These agents are interacting with knowledge as context as opposed to data inputs. And they can reason their way to a conclusion when they're interacting in so far as these workflows are concerned. So, for example, you can say, write these documents to a database. That begs a whole suite of questions. What fields in the database? How does the fields in the database map to this particular document? What do you want me to extract from this document? Write to the database. What database?
What's the pathway base, et cetera? So, the agents have the ability on the fly to reason through that imperative and get to an outcome that was so desired by the user who's sort of commanding them to do that particular job.

**Conor Bronsdon** (4:14)
And this is really interesting because I think most of us are used to deterministic code essentially, where, hey, we expect the output to remain the same. And this more non-deterministic model of an agent or typically non-deterministic, you get so much more capability as far as reasoning, but the array of results you may receive is more varied. And this becomes particularly interesting for folks in software engineering because as we've seen at Linear B, we've just released a 2024 bot automation research study that found that more than 13% of pull requests today are already bot created. And we expect that number to increase with this continued innovation in AI, more happening on the agentic AI side of things. How rapidly do you expect to see fully AI, non-deterministic models come through and actually commit code to a code base?