How to build reliable AI agents
With a little help from W&B Weave—and a lot of iteration
Created on April 2|Last edited on April 7
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Agents are to AI what websites are to the internet and apps are to smartphones—every business is going to build them. And not just one, but many, for everything from product information and marketing to operations, customer service, and hiring.
Agents turn AI from “a cool demo” into something actually useful. But let’s be honest: rolling them out in production is still a mess. They’re flaky, tough to debug, and hard to control. That’s exactly where W&B Weave comes in.
Here’s a closer look at why agents are so challenging—and how Weave’s capabilities give you the visibility and control to confidently launch them in production.
What is an AI agent anyway?
Think of an agent as an autonomous AI assistant that plans and executes multi-step tasks—using memory and tools—to achieve a goal. It’s not just answering a question, it’s figuring out how to answer it, which tools to use, what steps to take, and when to stop. It’s like a little LLM-powered task force working on your behalf.
Why should you care about agents?
Because agents are the leap from “cool AI demo” to “real productivity gains.” They're the reason AI might actually start doing your boring tasks—booking travel, writing boilerplate, fixing your flaky tests.
But there’s a catch:
Agents are easy to demo, hard to productionize
Sure, anyone can spin up a flashy agentic demo. But getting agents to reliably function in production? That’s a whole different ballgame. They’re often too inconsistent for real-world use—because agents aren’t like traditional software. They make their own decisions, plan their own steps, and rely on non-deterministic LLMs. That means your agent might commit basic mistakes a human wouldn’t, and it might come up with a slightly different plan every time you run it, even with the same input.
Traditional software development tools just aren’t designed for these probabilistic systems. When something goes wrong, you can’t simply trace a deterministic path between the inputs and the “correct” outputs. You have to consider the stochastic nature of the agent’s planning and execution—both in development and in production, where you’ll need to monitor for anomalies, fix them on the fly, and add those edge cases to your evaluation datasets.
In short, you need specialized observability and governance designed specifically for agents. We’ve added a host of new capabilities to W&B Weave to meet these needs. Let’s break them down.
Observability: Know what your agent is actually doing
With W&B Weave, you get real visibility into how your agents function. Here’s what that looks like:
Evaluations to accelerate iteration velocity
Agentic AI applications are complex and involve many moving parts, so both qualitative and quantitative metrics can be tricky to build and track. Weave speeds up this process by letting you evaluate agents with pre-built, third-party, or custom scorers—boosting iteration velocity.
You can customize Weave’s ready-made scorers to fit your agents or bring in external ones (or build your own from scratch). Weave’s Evaluation feature lets you zero in on specific examples—typically the toughest cases in your dataset—and compare runs to pinpoint the best-performing iteration. It also helps you build evaluation datasets from production traces and incorporate human annotations and feedback for ground truth.
Trace Trees that tell a clear story
Agents break tasks into sequences of steps—think of calling tools, reflecting on outputs, or retrieving relevant data—and run in a loop. The nesting can get pretty deep, making traditional call-stack views tough to navigate. With Weave Traces, you can visualize these complex rollouts to speed up iteration. Weave trace trees are purpose-built for agentic systems, letting you easily compare outputs, actions, and environment states across each step. That means it’s simpler to pinpoint issues, find hidden opportunities, and continuously improve your agent’s performance.
Trace views tailored to analyze agent calls
In addition to the hierarchical tree view, Weave provides two other views that simplify navigating agentic workflow traces. First, the code compilation view neatly organizes traces by automatically grouping calls to the same agent, allowing you to quickly assess aggregated metrics like the total number of calls made, how many calls were completed, and average latency per agent.
Additionally, the flame chart view offers a timeline visualization that clearly illustrates the timing and sequence of agent calls. Each call is represented as a horizontal bar, stacked vertically based on nested or overlapping execution. This layered view allows you to quickly identify concurrent activities, bottlenecks, and dependencies within your workflow, providing deep insights into orchestration, handoffs, performance, and efficiency.
Integrations: Framework-agnostic, future-proof setup
The agent ecosystem is evolving quickly, with new frameworks popping up constantly. Weave seamlessly supports them all—OpenAI Agents SDK, CrewAI, LangChain, LlamaIndex, DSPy, and more—so there’s no lock-in and no headaches. Let's spotlight our two latest additions: OpenAI Agents SDK and CrewAI.
OpenAI Agents SDK
OpenAI’s new Agents SDK is a lightweight yet powerful framework designed specifically for creating multi-agent workflows. Our pre-built integration between W&B Weave and the OpenAI Agents SDK makes developing these multi-agent applications simpler than ever. Getting started takes just three quick steps:
- Initialize Weave with your project name
- Add the Weave tracing processor to your OpenAI agent workflow
- Create and run your agents as usual
That’s it! Weave automatically captures detailed traces of every agent run—including inputs, LLM outputs, tool usage, metadata, and custom scores. Just follow the provided Weave dashboard link to visualize and analyze your agent traces. For more information, see our developer guide.
CrewAI
We also recently launched an integration with CrewAI, one of today’s most popular frameworks for multi-agent workflows and applications. With the new pre-built integration between CrewAI and W&B Weave, building agent crews has never been easier. Weave automatically logs all inputs, outputs, metadata, tool calls, actions, and state information at each step of your agent’s rollout. This detailed logging helps you quickly identify issues, evaluate agent quality and safety, and monitor performance in production. The integration supports both Crews and Crew Flows, empowering you to effortlessly build sophisticated multi-agent applications. For more details, check out our webinar with CrewAI.
With W&B Weave, your agentic development process stays future-proof, no matter which new frameworks emerge. Watch our webinar with CrewAI on how to build agentic systems using the new integration between CrewAI and W&B Weave.
Governance: Manage at scale and stay compliant
Even the smartest agent needs some guardrails and governance tools. Weave gives you the tools to stay in control, even when things go sideways.
Guardrails that actually work
Because LLMs are non-deterministic, you need to modify an agent’s inputs and outputs the moment harmful, inappropriate, or off-brand content is detected. Weave lets you do just that, enabling real-time adjustments to mitigate hallucinations and prompt attacks. With Weave Guardrails, you can detect hallucinations on every LLM call and automatically filter out inaccurate outputs. The built-in PII detection scorers also flag potential privacy issues, so you can stop them before they become a problem.
Registry and lineage for total reproducibility
When you run agent-based systems, you need to track which version of the agent ran, when it ran, and why it made certain decisions. Weave’s Registry stores models, datasets, and metadata, letting you reproduce any behavior—whether for debugging, auditing, or compliance. In practice, that means you can rebuild specific agent versions and configurations, then replay real production events to see exactly what happened. Weights & Biases serves as your system of record, granting you central access to model versions and lineage.
Proof that it works
Our CTO used Weave to build the one of the world’s top-ranked programming agents on the SWE-Bench Verified leaderboard. He achieved this by iterating fast—977 times total, averaging over 17 iterations a day—and Weave made that possible.
Getting started: It's simple
You don’t need a PhD in agent frameworks to get started with Weave. It’s literally three lines of code. Want a walkthrough? Check out our free course and whitepaper. Want to dive into the technical details? Our Weave docs are a great place to start.
If you're serious about building agents that actually work—ones you can deploy without holding your breath—then observability and governance aren’t optional. With Weave, you get both out of the box.
Productionize agents. Build something real with AI.
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