37 posts tagged with "agents"

Most teams that adopt reasoning models make the same mistake: they start using them everywhere. A new model drops with impressive benchmark numbers, and within a week it's handling customer support, document summarization, and the two genuinely hard problems it was actually built for. Then the infrastructure bill arrives.

Reasoning models — o3, Claude with extended thinking, DeepSeek R1, and their successors — are legitimately different from standard LLMs. They perform an internal chain-of-thought before producing output, spending more compute cycles to search through the problem space. That extra work produces real gains on tasks that require multi-step logic. It also costs 5–10× more per request and adds 10–60 seconds of latency. Neither of those is acceptable as a default.

LLMs are text generators. Your application needs data structures. The gap between those two facts is where production bugs live.

Every team building with LLMs hits this wall. The model works great in the playground — returns something that looks like JSON, mostly has the right fields, usually passes a JSON.parse. Then you ship it, and your parsing layer starts throwing exceptions at 2am. The response had a trailing comma. Or a markdown code fence. Or the model decided to add an explanatory paragraph before the JSON. Or it hallucinated a field name.

The industry has spent three years converging on solutions to this problem. This is what that convergence looks like, and what still trips teams up.

Most teams shipping LLM applications to production have a logging setup they mistake for observability. They store prompts and responses in a database, track token counts in a spreadsheet, and set up latency alerts in Datadog. Then a user reports the chatbot gave wrong answers for two days, and nobody can tell you why — because none of the data collected tells you whether the model was actually right.

Traditional monitoring answers "is the system up and how fast is it?" LLM observability answers a harder question: "is the system doing what it's supposed to do, and when did it stop?" That distinction matters enormously when your system's behavior is probabilistic, context-dependent, and often wrong in ways that don't trigger any alert.

Ninety percent of agentic RAG projects failed in production in 2024. Not because the technology was broken, but because engineers wired up vector search, a prompt, and an LLM, called it a retrieval pipeline, and shipped — without accounting for the compounding failure costs at every layer between query and answer.

Classic RAG is a deterministic function: embed query → vector search → stuff context → generate. It runs once, in one direction, with no feedback loop. That works when queries are clean single-hop lookups against a well-chunked corpus. It fails spectacularly when a user asks "compare the liability clauses across these five contracts," or "summarize what's changed in our infra config since the Q3 incident," or any question that requires synthesizing evidence across documents before forming an answer.

Ask any team that's shipped a real agentic system what the hard part was. Almost none of them will say "the LLM call." The core loop that every production agent runs is nearly identical, whether it's Claude Code, Cursor, or a homegrown financial automation tool. The interesting engineering — the part that separates a working agent from a runaway cost center — lives entirely outside that loop.

One team started running an agent loop at $127 per week. Four weeks later, the bill hit $47,000. An uncontrolled loop with no token ceiling had compounded every iteration into a financial catastrophe. The model kept running. Nobody told it to stop.

Most agent frameworks default to the same action model: the LLM emits a JSON blob, the host system parses it, calls a tool, returns the result. Repeat. It's clean, auditable, and almost universally used — which is exactly the problem. For anything beyond a single tool call, this architecture forces you to write scaffolding code that solves problems the agent could solve itself, if only it were allowed to write code.

There's a different approach: give the agent a Python interpreter and let it emit executable code as its action. One published benchmark shows a 20% higher task success rate over JSON tool-calling. An internal benchmark shows 30% fewer LLM round-trips on average. A framework built around this idea hit #1 on the GAIA leaderboard (44.2% on validation) shortly after release. The tradeoff is a more complex execution environment — but the engineering required is tractable, and the behavioral gains are real.

Your monitoring stack tells you everything about request rates, CPU, and database latency. It tells you almost nothing about whether your LLM just hallucinated a refund policy, why a customer-facing agent looped through three tool calls to answer a simple question, or which feature in your product is quietly burning $800 a day in tokens.

Traditional observability was built around deterministic systems. LLMs are structurally different — same input, different output, every time. The failure mode isn't a 500 error or a timeout; it's a confident, plausible-sounding answer that happens to be wrong. The cost isn't steady and predictable; it spikes when a single misconfigured prompt hits a traffic wave. Debugging isn't "find the exception in the stack trace"; it's "reconstruct why the agent chose this tool path at 2 AM on Tuesday."

This is the problem LLM observability solves — and the discipline has matured significantly over the past 18 months.

The first time your agent silently retries the same broken tool call three times before giving up, you realize that "just add tools" is not a production strategy. Tool calling unlocks genuine capabilities — external data, side effects, guaranteed-shape outputs — but the agentic loop that makes it work has sharp edges that don't show up in demos.

This post is about those edges: how the loop actually runs, the formatting rules that quietly destroy parallel execution, how to write tool descriptions that make the model choose correctly, and how to handle errors in a way that lets the model recover instead of spiral.

One company shipped 7,949 AI agents. Fifteen percent of them worked. The rest failed silently, looped endlessly, or contradicted themselves mid-task. This is not a fringe result — enterprise analyses consistently find that 88% of AI agent projects never reach production, and 95% of generative AI pilots fail or severely underperform. The gap between a compelling demo and a reliable system is not a model problem. It is an architecture problem.

The engineers who are shipping agents that actually work have converged on a set of structural decisions that look nothing like the toy examples in framework tutorials. This post is about those decisions: where the layers are, where failures concentrate, and why the hardest problems are not about prompts.

The most surprising thing about LLM function calling failures in production is where they come from. Not hallucinated reasoning. Not the model picking the wrong tool. The number one cause of agent flakiness is argument construction: wrong types, missing required fields, malformed JSON, hallucinated extra fields. The model is fine. Your schema is the problem.

This is good news, because schemas are cheap to fix.

Most AI teams are measuring the wrong things, in the wrong way, with the wrong people involved. The typical evaluation setup looks like this: a 1-to-5 Likert scale, a handful of examples, and a junior engineer running the numbers. Then someone builds an LLM judge to automate it—and wonders why the whole thing feels broken six months later.

LLM-as-a-judge is a powerful pattern when done right. But "done right" is doing a lot of work in that sentence. This post is a concrete guide to building evaluators that correlate with real quality, catch real regressions, and survive contact with production.

Most generative AI projects fail — not because the models are bad, but because teams make the same predictable mistakes at every layer of the stack. A 2025 industry analysis found that 42% of companies abandoned most of their AI initiatives, and 95% of generative AI pilots yielded no measurable business impact. These aren't model failures. They're engineering and product failures that teams could have avoided.

This post catalogs the pitfalls that kill AI projects most reliably — from problem selection through evaluation — with specific examples from production systems.