LangChain

2026-10-30

CODING · 1 SOURCE · NVIDIA Developer

NVIDIA publishes NemoClaw blueprints to assemble secure, optimized agent stacks

NVIDIA introduced NemoClaw, a collection of open blueprints that assemble a full-stack agent platform by combining tuned agent harnesses, Nemotron models, NeMo optimization tools (including NeMo Relay), and the OpenShell secure runtime. The blueprints support OpenClaw, Hermes Agent, and LangChain Deep Agents, include a LangChain-tuned Nemotron 3 Ultra profile (near-frontier performance at about 10x lower cost per run), and provide GitHub examples, a quickstart, and a DLI course.

7.0

2026-10-26

CODING · 1 SOURCE · InfoQ AI, ML & Data Engineering

InfoQ outlines evolution of agentic harnesses from chains to graphs to code

An InfoQ article summarizes three paradigms for building agentic harnesses around LLMs — chains (linear workflows), graphs (explicit control-flow with nodes/edges), and code (sandboxed, execution-first systems) — and argues the field has moved from ad‑hoc glue to production-grade, observable architectures. The piece highlights tradeoffs (latency, fragility, resilience) and recommends treating LLM outputs as untrusted, decoupling planning from execution, and investing in observability and minimal necessary abstractions.

6.0

2026-10-05

CODING · 1 SOURCE · AWS Machine Learning

Amazon Bedrock Managed Knowledge Bases supports agentic retrieval; LangChain walkthrough

A technical walkthrough demonstrates using LangChain with Amazon Bedrock Managed Knowledge Bases to compare standard single-shot retrieval (Retrieve API) and multi-step agentic retrieval (AgenticRetrieveStream API). The post explains architecture, IAM permissions, boto3 version requirements (agentic_retrieve_stream requires boto3 >= 1.43.32), and trade-offs including cost and trace event inspection.

6.0

2026-10-04

CODING · 1 SOURCE · InfoQ AI, ML & Data Engineering

Pizza Bot: AWS engineers open-source an inbox-style, local-first agent runner

AWS engineers Joseph Dolivo and Igor Fil published Pizza Bot as an Apache-2.0 open-source, self-hosted application that lets AI agents run background workflows and return results via an email-style inbox. The client-server tool supports scheduled and webhook triggers, delegation to specialist workers, multiple model providers and MCP servers, and stores agent state and data locally under user control.

6.0

2026-10-01

CODING · 1 SOURCE · AWS Machine Learning

Amazon Bedrock AgentCore guide for ambient agents with event-driven, human-in-the-loop workflows

AWS published a walkthrough and reference implementation showing how to build "ambient" agents on Amazon Bedrock AgentCore that react to event streams (S3, EventBridge, scheduled tasks), pause for human approval via a single ask_human tool, and resume—using AgentCore Runtime plus serverless AWS components (Lambda, DynamoDB, SQS) and Bedrock foundation models. The post includes a sample deployment path requiring AWS CLI, CDK v2, Docker, and an ECR-hosted agent container, and notes session timeouts align with Lambda's 15-minute limit in the reference.

6.0

2026-10-01

MODELS · 1 SOURCE · InfoQ AI, ML & Data Engineering

TypeSafe AI launches Jev, a decision-only model returning typed probabilities

TypeSafe AI released Jev, the first of its "System One" models: a decision-only model that returns typed, probabilistic answers (Choice, Score, Noul) instead of natural language, evaluating multiple typed questions in parallel and emitting probability distributions with confidence values. Jev has a 32,000-token context window, quoted input cost of $0.042 per million tokens (output free), end-to-end latency of 70–500 ms, uses a training method called Reinforcement Learning for Calibrated Decisions, and saw rapid early integrations from Vercel, Netlify and LangChain.

8.0

2026-09-25

CODING · 1 SOURCE · InfoQ AI, ML & Data Engineering

InfoQ explains the 'agent harness' concept and compares HaaS vs self‑managed builds

An InfoQ article defines the 'agent harness' as the non‑model engineering around an AI model that makes an agent production‑ready, split into development (memory, tools, retrieval, orchestration) and operations (observability, guardrails, cost/control). It compares Harness‑as‑a‑Service (examples: AWS AgentCore) with self‑managed stacks (examples: LangChain + Agent Router/Envoy AI Gateway on Kubernetes), walks through building a sample agent (FinBot), and advises starting minimal and evolving the harness.

6.0

2026-09-18

COMPANIES · 1 SOURCE · InfoQ AI, ML & Data Engineering

WSO2 launches Agent Manager (GA) to govern multi-framework AI agents

WSO2 announced the general availability of the open-source WSO2 Agent Manager, a centralized platform for identity, governance, security controls, and operational oversight of AI agents across models, frameworks, and deployment environments. The GA release adds deeper agent identity features, governance for Model Context Protocol (MCP) interactions, a Kubernetes-native sandboxed runtime, OpenTelemetry tracing, lifecycle controls (including suspension), and over 40 built-in policies for things like PII masking and rate limiting.

7.0

2026-09-17

STARTUPS · 1 SOURCE · TechCrunch AI

Startups and labs are using AI tools to monitor agent swarms

As AI agents take on longer, higher-volume tasks, companies and safety labs are building AI-based monitors to oversee agent behavior—examples include Apollo Research’s Watcher and Goodfire’s Silico—while investors pour funding into observability startups. Researchers warn this approach can help scale oversight but creates adversarial dynamics where malicious agents may try to deceive monitoring AIs.

7.0

2026-09-15

CODING · 1 SOURCE · AWS Machine Learning

Optimizing cost and latency with Amazon Bedrock prompt caching

An AWS Machine Learning post describes prompt caching in Amazon Bedrock, saying it can cut input token costs by up to 90% when the same context is repeatedly sent to foundation models. The article walks through six practical prompt caching scenarios for the Converse API: message content, system prompt, tool definition, mixed TTL, tenant isolation, and LangChain integration.

4.0