One of the most common failure modes in AI agent deployment is the handoff: the moment when an AI agent reaches the limit of its authority or capability and needs to pass the task to a human. Done well, the human picks up exactly where the agent left off, with full context on what was … Read more
Browser automation has existed for years in the form of tools like Selenium and Playwright, but these tools require precisely scripted actions — click this element, wait for this selector, fill this input field. They break the moment the website changes its layout or adds a new step. Browser-use AI agents work differently: they see … Read more
Deploying an AI agent is straightforward. Knowing what it is actually doing after deployment is significantly harder. Unlike a traditional software service where logs tell you exactly what code ran and what data it processed, AI agents make probabilistic decisions — they choose which tool to call, what to search for, how to interpret results … Read more
The default AI agent has no memory. Each conversation starts from scratch — the agent has no knowledge of what you discussed yesterday, what preferences you expressed last week, or what context you built up over months of interactions. For personal productivity assistants, customer-facing AI tools, and any application where the relationship between the AI … Read more
CrewAI is an open-source framework for building multi-agent AI systems — AI setups where different agents take on defined roles, work on separate parts of a task, and pass results between each other like a coordinated team. Rather than asking a single AI to handle every aspect of a complex workflow, CrewAI lets you define … Read more
Evaluation (“evals”) is how AI practitioners measure whether their models and prompts are working as intended. The term sounds academic but the practice is straightforward: you define what good output looks like, test your system against a set of representative inputs, and measure how often the output meets your standard. You do not need a … Read more
AI language models produce confident-sounding output regardless of whether they are right. The same fluent, authoritative tone appears whether the model is reporting a well-established fact or confabulating something it has no reliable knowledge about. Confidence scoring — techniques for estimating how certain a model’s output actually is — addresses this problem by surfacing uncertainty … Read more
Shipping AI-powered features without measuring their accuracy is like shipping code without testing it. The consequences are similar: bugs in production, user frustration, and the compounding cost of discovering problems after they have affected real users. AI evaluation frameworks provide the testing infrastructure for AI systems — structured methods for measuring whether AI outputs meet … Read more
AI language models have training data cutoffs — they do not know what happened after their training ended. For business tasks involving current market data, recent competitor activity, latest regulations, breaking news, or any information that changes over time, a model’s training knowledge is unreliable. Connecting AI to real-time web search grounds it in current … Read more
RAG systems retrieve relevant content by searching for it — but traditional search engines look for keyword matches. The content you need is not always described using the same words as the query: a question about “employee leave entitlement” should retrieve documents that discuss “annual leave policy” even though none of those exact words overlap. … Read more