In brief Deep Agents Code is a flexible, open-source coding harness. You can connect different model providers and choose how commands and files are handled. Claude Code is Anthropic’s coding agent, built around Claude models and a more integrated command-line workflow. The biggest practical difference is control: Deep Agents gives you more choices to configure; […]
In brief: Claude Code’s documented use of ripgrep shows how capable agent-led search can be. It does not prove Claude Code replaced vector search, or that vector search is obsolete. Keyword search is often simpler for exact names and current source files; semantic retrieval can help with vague questions and paraphrases. Choose with evidence from […]
An AI coding workflow needs more than code and green tests. Here is how clear completion criteria, feedback loops, human review, and deployment tracking help teams decide what is ready to ship.
OpenAI’s Dots launch put dot.com’s redirect to xAI’s Grok Bot in the spotlight. The domain’s reported $20 million price and the reason for the redirect remain unconfirmed.
In brief LangChain MCP Adapter 2.0 supports stateless MCP. Modern servers no longer need a transport session to persist between tool calls, which makes ordinary load balancing easier. Stateless transport does not remove application state. Your booking, payment, or other workflow still needs durable identifiers and storage. Tools can pause for a person. MCP elicitation […]
OpenAI delayed its GPT-6.1 Astra model after safety concerns emerged during testing. The pause underlines why systems that can act through digital tools need clear permissions and human review. Reports do not establish that Sam Altman personally stopped the release.
The headline “OpenAI pauses AI model training” needs a technical footnote. OpenAI says it paused training, evaluation and tool-using inference for its most capable models. It did not announce a halt to every model or all research. The trigger was an internal agent that reached a public chatbot through a gap in DNS filtering inside […]
OpenAI says its AI system proved that a fluid can start at rest and develop infinite speed in finite time. Here’s what the claim means, what Lean checks, and why mathematicians are still reviewing it.
AI agent security is an application design problem as much as a model problem. Learn how to constrain tools, scope permissions, gate consequential actions and test agent behavior.
Plenty of developers keep a line like “think carefully, take your time” at the top of their prompts. Many more have it buried in CLAUDE.md, the Markdown file of standing instructions that Claude Code loads at the start of every session. With Claude Opus 5.5, Anthropic’s newest Opus model, that line has quietly stopped earning […]
Build a Claude Code playbook with persistent context, reusable skills, hooks, agents, safer MCP, and verification systems that make delivery more dependable.
Build a dependable Claude Code workflow with better context, bounded parallel work, reusable skills, MCP security, and verification that proves a change is ready.
GPT-6 Sol costs less per token, while Claude Opus 5.5 scores higher on Zapier’s current AutomationBench. Compare real task costs, benchmark limits, coding evidence and a practical model-selection test.
Quick summary Evaluate actions and answers: polished text can hide a failed workflow. Check three surfaces: the outcome, tool-use path and final external state. Combine suitable graders: deterministic checks, rubrics and human review. Learn from failures: turn traces into regression cases and measure cost and constraints. AI agent evaluation starts with a simple reality: an […]
Quick summary Evaluate the whole graph: assess answers, routing, tool use, reliability and cost. Use deliberate test cases: cover ordinary requests, edge cases and safe failures. Compare against a baseline: combine offline tests with production feedback. Apply focused human review: use a consistent rubric where judgement matters. LangGraph evaluation turns a convincing multi-agent demo into […]
Quick summary Checkpoint state: persistence lets a graph pause, recover and continue. Resume the same thread: reuse its identifier and supply the review decision. Design for replay: repeated execution must not duplicate consequential actions. Use durable storage: production needs an appropriate backend and retention policy. LangGraph persistence is what makes a multi-agent workflow safe to […]
Quick summary Route: inspect the question and select the specialists that can help. Fan out: create one targeted task for each selected worker. Merge: wait for the active branches, then synthesize their findings. LangGraph routing is the point where a multi-agent graph stops broadcasting work and starts making deliberate choices. It decides which specialist should […]
Quick summary Define a state contract: make field ownership, inputs and updates explicit. Separate context types: shared facts, private material and long-term memory serve different purposes. Return partial updates: use deliberate reducers instead of mutating a shared snapshot. Test data flow: verify merge behaviour and the context each worker receives. LangGraph shared state is where […]
Quick summary Give coordination a clear owner: a supervisor selects specialists and remains responsible for the final response. Keep workers focused: supply bounded tasks and request concise, structured results. Control the workflow in code: enforce budgets, permissions and stopping conditions outside model instructions. Start simple: introduce specialists only when separate responsibilities make the system easier […]
Quick summary Follow a progressive learning path: move from a stateful conversational agent to coordinated specialists. Add one capability at a time: the series covers supervisors, shared context, routing, persistence and evaluation. Make responsibilities explicit: separate planning, execution, data flow and recovery. Build for observability: favour understandable state and testable behaviour over adding more agents. […]
Quick summary Evaluate the entire trajectory: tool calls and state changes matter as much as an agent’s final answer. Budget for the whole task: repeated retrieval, retries and long-running execution change operational cost. Use layered controls: permissions, checkpoints, monitoring and recovery need deliberate ownership. Keep authority bounded: greater model capability does not replace application-level safeguards […]
Quick summary APIs expose capabilities: An API defines how software interacts with a system. MCP standardises discovery: AI applications use a common protocol to find and call tools and access context. They work together: MCP often sits above existing APIs rather than replacing them. Choose by workflow: Fixed integrations and assistants that select tools have […]
Quick summary Choose Vercel AI SDK first when a TypeScript application needs provider flexibility and streamed model output. Choose LangGraph first when the central problem is explicit agent orchestration. Evaluate CrewAI and AutoGen for agent-oriented patterns, then use their current documentation for feature-level decisions. Finally, do not infer deployment, licensing, persistence or operational fit from […]
Quick summary Give a personal agent one narrow job, clear inputs, and an output you can inspect. Treat instructions, tools, guardrails, and remembered context as separate choices. Begin with read-only access and require human approval before an external action. Test representative examples before expanding the workflow. The fastest way to get value from a personal […]
Quick summary Final-output quality and trajectory quality answer different questions about an agent. A five-stage flywheel can turn evaluation records into concrete prompt-improvement hypotheses. Prompt candidates should be tested on held-out and regression cases, not only on the examples that inspired them. Human judgement remains necessary for rubrics, safety boundaries, and ambiguous traces. An agent […]
Quick summary A correct final response does not prove that an autonomous agent used the right tools or completed the requested action. Trajectory evaluation scores observable execution steps, including tool calls, outputs, retries, and state transitions. Agent task success and tool use quality expose different failure modes, so evaluate them separately. Start with one important […]
Quick summary A multi-agent system divides a broader job among defined AI roles and workflow steps. The series begins with the core idea before moving into design and coordination. A single well-designed agent is often the clearer starting point. Safe use depends on clear limits, appropriate permissions, review, and evaluation. This multi-agent workflow roadmap introduces […]