Mia: You know, it feels like every other day there's a new buzzword, a new framework, a new 'breakthrough' when we're talking about AI agents. But really, what's the deal when you try to build these things for actual, serious applications out in the wild?
Mars: Oh, absolutely! It's like a total deja vu for me, seriously. I'm flashing back to the wild west days of the web, like 1993. Developers were literally just *stitching* HTML and CSS together, no rulebook, no proper way to do things. Then, bam, 2013 rolls around, Facebook drops React, and it's not just a library, it's a whole new *philosophy* for how we build. Fast forward to 2025 with AI agents? We are *right back* in that raw HTML phase. We've got tons of cool experiments, don't get me wrong, but zero established philosophy. And honestly, it's a bit worrying because some big names, like OpenAI with their swarm stuff and Microsoft with Autogen, are really pushing these multi-agent architectures that, let's be real, can be super fragile when you try to use them for real work.
Mia: Whoa, that's a pretty bold statement, coming from you. You're basically saying some of these widely celebrated concepts from the big guns like OpenAI and Microsoft might actually be fundamentally broken for production? What makes you so sure about that, and what exactly is so problematic with these multi-agent ideas?
Mars: Well, here's the thing. Multi-agent systems *promise* this amazing parallelism by slicing tasks into little sub-agents, right? But what actually happens is you run straight into error compounding. Every single one of those sub-agents makes implicit decisions, and without a fully shared context, they just totally misunderstand their tiny little subtasks. You could end up with one sub-agent building a background that looks suspiciously like Super Mario Bros, while another one's trying to make a bird that definitely *doesn't* flap like a Flappy Bird. Then the poor main agent has to somehow merge these totally inconsistent artifacts. That fragility? That's precisely why I'm so against naive multi-agent designs in serious production environments.
Mia: So, if the current landscape is really this riddled with missteps and, dare I say, flawed approaches, what's the secret sauce we're missing? What's that foundational piece we need to actually introduce to get us out of this 'raw HTML' phase of agent building and finally hit true reliability?
Mars: The secret sauce, my friend, is what I've been calling Context Engineering. Prompt engineering was all about meticulously crafting that one perfect phrase to make an LLM do your bidding. Context engineering is the next level: it's about dynamically managing context within an automated system. It's making sure that an agent, no matter how long it's been running, always has *all* the nuance it needs. Seriously, it's the number one task for any engineer trying to build reliable AI agents that have to survive multiple turns.
Mia: Okay, let's really dig into that Flappy Bird example you mentioned, because it sounds pretty telling. How does what seems like a perfectly logical task breakdown suddenly lead to catastrophic failure and those compounding errors in a naive multi-agent setup?
Mars: Alright, picture this: you ask an agent to build a Flappy Bird clone. So you, logically, split it. Subtask 1: create the background with green pipes and hit boxes. Subtask 2: build the bird that moves up and down. A naive system just fires up two sub-agents with only those messages. Sub-agent 1, bless its heart, builds a Mario Bros-style background because it completely missed the whole 'Flappy Bird aesthetic' memo. Sub-agent 2 makes a bird that isn't even a proper game asset and just doesn't flap correctly. Without shared context of the original task *and* all the previous decisions, each sub-agent just drifts off into its own little world. When you try to combine their results, you end up with mismatched assets that just don't mesh. And trust me, trying to fix that mess retroactively is an absolute nightmare.
Mia: So it's not just a one-off misunderstanding, it's like a chain reaction of errors all stemming from this lack of shared context. How does this kind of fragility actually show up in real-world production systems, and why is it so incredibly difficult to recover from once it starts?
Mars: In real production, conversations are multi-turn, tool calls change the entire state of things, and every single decision can subtly twist the interpretation. If one sub-agent misreads just one tiny detail, every single step downstream compounds that error. It's like trying to build a house when your foundation stones are already misaligned. You can't just go fix one brick without risking the entire structure collapsing. The only way to stop it is to make absolutely sure every action is fully informed by *all* prior decisions.
Mia: It's crystal clear that simply breaking down tasks isn't cutting it. Which brings us perfectly to your first core principle for building agents that actually work. What is it, and how does it directly tackle these context-related pitfalls we've been talking about?
Mars: Principle One: share context, and share *full agent traces*, not just individual messages. This means every single sub-agent sees the entire history – all the decisions, all the tool calls, all the conversations that happened before it even got called. You simply cannot treat the original task as some static prompt. You absolutely *must* share even the intermediate reasoning and actions so all the sub-agents are working from the exact same mental model.
Mia: So, why isn't just copying the original task description enough? Could you really break down the critical difference between sharing just a message versus sharing a full agent trace, and why that latter one is just non-negotiable?
Mars: Copying the original task? That's just a static starting point, you know? It totally misses all the dynamic nuance that gets picked up during the actual run. Full agent traces include all the adjustments, the clarifications, the tool outputs, even the attempts that failed! It's like trying to understand a super complex team project by just reading a few emails. You're missing the intense whiteboard discussions, the detailed design documents, all the bug reports. With the full timeline, you actually see *why* a decision was made. You avoid repeating mistakes and, crucially, you don't drift off concept.
Mia: Does implementing these full agent traces completely zap the consistency problem we saw in that Flappy Bird example, or are there still some sneaky pitfalls lurking even with all that context?
Mars: Full traces go a seriously long way, but they don't magically eliminate *all* ambiguity. Sub-agents might still make conflicting implicit decisions even if they're looking at the exact same context, especially if they're not constrained. You could still end up with different visual styles because each sub-agent decides to prioritize different aspects. And that, my friend, brings us to Principle Two: actions carry implicit decisions, and conflicting decisions carry bad results.
Mia: It sounds like even with all the context in the world, there's another layer of complexity that can just throw a wrench in the works and lead to inconsistencies. So, what is this second crucial principle, and how does it address these remaining challenges that even comprehensive context sharing can't quite resolve?
Mars: Principle Two says: every action an agent takes *is* a decision. When you have multiple agents acting in parallel without a shared decision framework, they're embedding all these hidden assumptions. And guess what? Those conflicting assumptions are exactly what produce inconsistent results. The cure? Minimize parallel decision threads. The simplest architecture that actually obeys this rule is a single-threaded linear agent, where each step builds on the last, preserving both context *and* decision coherence.
Mia: Even with full context sharing, you're saying the Flappy Bird problem, specifically the inconsistent visual styles, still sticks around. How does your second principle actually explain this persistent failure point?
Mars: Even when those two sub-agents see the exact same context, they're still making their own decisions about style, color palette, mechanics. And here's the kicker: those implicit choices aren't communicated! So when you try to merge their outputs, you just get this stylistic clash. A linear agent, on the other hand, makes decisions in sequence. The background is finalized *before* the bird is made, so the bird can match the background style perfectly. There's no parallel divergence, no stylistic wrestling match.
Mia: From a developer's perspective, how does really grasping this principle fundamentally steer them away from these multi-agent pitfalls and towards designs that are much more robust, much more predictable?
Mars: It totally shifts the focus, you know? Instead of building these super clever frameworks for spawning tons of agents, you start thinking about designing clear, sequential workflows where each action intentionally follows from the last. Instead of just trying to parallelize everything under the sun, you actually ask yourself: Which parts *really* need parallelism, and which parts can actually be handled in a single thread to maintain consistency? You're choosing reliability over some naive idea of speed.
Mia: A single-threaded approach sounds incredibly robust, but what about those super large, long-duration tasks where context windows might just blow up? How do we scale these principles to handle significantly more complex, extended challenges without losing our minds?
Mars: Ah, for truly long-running tasks, we bring in the big guns: context compression. We add a dedicated LLM whose entire job is to compress the history of actions and conversation into a super concise summary of the key details, events, and decisions. That summary then serves as the context for the *next* phase. It takes some serious engineering effort to tune what information to keep, and sometimes we even fine-tune a smaller model just to do the compression effectively. It's a bit of a dance, but totally worth it.
Mia: Could you walk us through that concept of using a dedicated LLM for context compression? How does that actually allow agents to effectively handle vast amounts of information over extended periods without losing coherence or, more importantly, violating your core principles?
Mars: Imagine trying to compress a ten-hour meeting into a ten-minute briefing that somehow covers all the critical decisions, action items, and contextual notes. That's what the compression model does! It distills all the extraneous chatter and just retains what truly matters. Then, the agent can pick up its work with that distilled context. You're still sharing a coherent decision trail, it's just in a much more manageable, bite-sized chunk. It keeps the whole system single-threaded and consistent without blowing past those pesky token limits.
Mia: And these principles are actually in play in real-world systems, right? Let's talk about Claude Code's subagents or the evolution of Edit Apply Models. How do these examples really show the careful trade-offs and design choices that are made to ensure reliability based on these principles, especially when parallelism is so tempting?
Mars: Oh, absolutely. Take Claude Code, as of June 2025 – it spawns subtasks, but it *never* runs them in parallel with the main agent. The sub-agent just answers a specific question with a super clear scope, totally avoiding any style or context drift. It keeps that investigative work out of the main history, extending the trace length without risking consistency. Now, Edit Apply Models in early 2024, they used a large model to propose diffs and a small model to apply them. But even that two-step split still fractured the decision context. Today, we do the editing and applying in one single model action to maintain that beautiful coherence.
Mia: It's fascinating to see these principles really guiding practical agent design. But there's always this persistent allure, this dream of true multi-agent collaboration, where agents are just chatting with each other like humans. What's your take on that vision, and is it even remotely achievable with today's technology?
Mars: Ah, the agents talking like humans dream! The idea of multiple agents engaging in proactive discourse to resolve conflicts is super appealing, but right now? It's just plain fragile. Human teams, we rough out differences through negotiation, right? We leverage common ground, nuanced communication. Agents just totally lack that efficiency. Current multi-agent setups, they disperse decisions and really struggle with cross-agent context passing. Until we crack that nut, multi-agent collaboration in 2025 is honestly more of a mirage.
Mia: You're comparing it to how humans resolve merge conflicts – which definitely requires some non-trivial intelligence. What makes human communication so incredibly efficient in that regard, and why are current AI agents so far from replicating that level of nuanced, long-context discourse?
Mars: Humans? We use shared conventions, we infer priorities, we read between the lines, and we adapt on the fly. We build a shared mental model almost instinctively. Agents today? They need explicit context and guardrails for days. Without that, multi-agent dialogue either falls apart spectacularly or just loops indefinitely. We simply haven't built the protocols or the context-passing mechanisms they desperately need yet.
Mia: So, despite all the enthusiasm buzzing around multi-agent systems, you're essentially saying it's a bit of a distraction from focusing on single-agent reliability. Where should the focus really be to unlock true parallelism and efficiency in the future, if not through immediate multi-agent collaboration?
Mars: The real breakthrough, I genuinely believe, will come from improving single-threaded agents' ability to communicate with humans *clearly*. As we refine that interface and get better and better at context engineering, cross-agent collaboration will just follow naturally. When a single agent can explain its reasoning flawlessly to a human, then stitching together multiple agents with that same level of clarity becomes incredibly straightforward.
Mia: As we wrap up this incredibly insightful chat, what's the overarching message you really want developers and researchers to take away from this discussion about building the next generation of truly reliable and intelligent AI systems?
Mars: Here's the kicker: reliability is *not* found in buzzwords or all that parallel agent hype. It comes from two core philosophies: rigorous context engineering and truly respecting that every action carries a decision. Embracing full context traces and single-threaded linearity might seem a bit constraining, I know, but trust me, it is *the* path to robust, long-running agents. Only then can we hope to scale to true multi-agent collaboration without falling into that dreaded fragility. That's how we build reliable LLM agents: context engineering over multi-agent fragility, every single time.
