This guide provides a comprehensive walkthrough on using Google's Gemma 4 model to build autonomous AI agents through tool calling. It explores how this feature enables models to move beyond simple text generation by interacting with external APIs and systems via structured function calls.
Key topics covered in the article include:
- The mechanics of the tool calling loop, from reasoning and selection to execution and final response.
- Setting up a Python development environment using Hugging Face and necessary libraries like transformers and torch.
- Defining JSON schemas for tools to ensure precise model understanding.
- Implementing a full agent workflow by parsing function call responses and executing Python functions.
- A practical end-to-end demonstration of building a weather lookup agent.
- Managing multi-turn conversations through state management and conversation history.
- Best practices for production deployment, including argument validation, execution timeouts, and logging.
Key topics covered in the article include:
- The mechanics of the tool calling loop, from reasoning and selection to execution and final response.
- Setting up a Python development environment using Hugging Face and necessary libraries like transformers and torch.
- Defining JSON schemas for tools to ensure precise model understanding.
- Implementing a full agent workflow by parsing function call responses and executing Python functions.
- A practical end-to-end demonstration of building a weather lookup agent.
- Managing multi-turn conversations through state management and conversation history.
- Best practices for production deployment, including argument validation, execution timeouts, and logging.
A real-time visualization tool for Claude Code and Codex agent orchestration that makes complex agent behaviors visible through interactive node graphs. It allows developers to monitor how agents think, branch, and coordinate during execution, facilitating easier debugging of tool call chains and reasoning processes.
- Live agent visualization via an interactive node graph with real-time streaming
- Concurrent support for Claude Code and Codex runtimes
- Integrated VS Code extension for direct workspace monitoring
- Interactive canvas with pan and zoom capabilities to inspect details
- Timeline, transcript panels, and JSONL log file replay functionality
- Live agent visualization via an interactive node graph with real-time streaming
- Concurrent support for Claude Code and Codex runtimes
- Integrated VS Code extension for direct workspace monitoring
- Interactive canvas with pan and zoom capabilities to inspect details
- Timeline, transcript panels, and JSONL log file replay functionality
An analysis of the recent Claude Code source code leak, focusing on architectural patterns rather than just security drama. The article explores how a massive TypeScript codebase reveals sophisticated design choices for building reliable agentic systems. It covers memory management strategies like skeptical verification and semantic consolidation, efficient tool orchestration through permission gates and prompt cache sharing, and multi-agent coordination models that optimize token costs.
- Three-layer memory systems using indexes to prevent context entropy
- Background processes for autonomous memory pruning and merging
- Granular permission gating and large result offloading in tool architecture
- Cost-effective multi-agent communication via prompt cache sharing
- Risk classification tiers for safe autonomous operations
- Three-layer memory systems using indexes to prevent context entropy
- Background processes for autonomous memory pruning and merging
- Granular permission gating and large result offloading in tool architecture
- Cost-effective multi-agent communication via prompt cache sharing
- Risk classification tiers for safe autonomous operations
Splunk .conf in Denver Sept 2026: The core theme focuses on "Agentic AI"—the transition toward autonomous workflows in security, observability, and operations to manage increasing data complexity and digital risk.
**Sessions**
* **Powering the Rise of the Agentic Enterprise:** Transforming security/ops with scalable, trust-based AI workflows.
* **See Inside Your Agentic AI Applications:** Tracing Python agent interactions to detect hallucinations and tool failures.
* **The Future is Here: What’s New in Splunk Security:** Transitioning to an "Agentic SOC" via automated orchestration.
* **The Platform for Your Agentic Enterprise:** Unlocking cross-domain insights through unified data and AI.
* **What's New in Platform:** Using a unified data fabric for predictive, agentic operations.
* **5 ML Algorithms in Under 5 Lines of Code:** Reducing alert fatigue using simple SPL/MLTK implementations.
* **Accelerate Digital Resilience (Cisco & Splunk):** Maximizing ROI through integrated ecosystem use cases.
* **AI Troubleshooting and Remediation Agents:** Using AI SRE agents to accelerate root cause analysis.
* **Accelerate SmartStore Searches with VAST Data:** Reducing storage costs via InfiniteCache integration.
* **The Cisco & Splunk Partner Playbook:** Strategies for scaling business in the AI era.
* **Accelerating Your Journey to Agentic Observability:** Navigating 4 steps toward agent-driven monitoring.
* **A Compromised Account Alerting System:** Automating detection of suspicious user behavior.
* **Advanced Threat Detection (DSDL & RBA):** Using machine learning for behavioral risk scoring.
* **Advancing Security Operations with ESCU Innovations:** Leveraging new high-fidelity threat intelligence content.
* **Agentic Data Ingestion (Anthropic's Claude):** Automating Splunk Add-On development using GenAI.
* **Agentic Detection Posture Management:** Using AI to rapidly identify and close detection gaps.
* **Agentic Incident Command:** Deploying coordinated AI agent teams for full incident lifecycles.
* **Hands-on Agent Attack Analysis:** Building anomaly detections from raw agent telemetry.
* **Build Your Own SOC AI Agent (Workshop):** Hands-on guide to building custom agents with guardrails.
* **Triage and Response at Scale:** Using Triage and SOP Agents for automated incident response.
* **Agents Are Easy, Trust Is Hard:** Implementing governance in cohesive agentic operations via Cisco Data Fabric.
* **AI Assistant V2 Unlocked (Workshop):** Hands-on with Agent Mode and organizational knowledge integration.
**Sessions**
* **Powering the Rise of the Agentic Enterprise:** Transforming security/ops with scalable, trust-based AI workflows.
* **See Inside Your Agentic AI Applications:** Tracing Python agent interactions to detect hallucinations and tool failures.
* **The Future is Here: What’s New in Splunk Security:** Transitioning to an "Agentic SOC" via automated orchestration.
* **The Platform for Your Agentic Enterprise:** Unlocking cross-domain insights through unified data and AI.
* **What's New in Platform:** Using a unified data fabric for predictive, agentic operations.
* **5 ML Algorithms in Under 5 Lines of Code:** Reducing alert fatigue using simple SPL/MLTK implementations.
* **Accelerate Digital Resilience (Cisco & Splunk):** Maximizing ROI through integrated ecosystem use cases.
* **AI Troubleshooting and Remediation Agents:** Using AI SRE agents to accelerate root cause analysis.
* **Accelerate SmartStore Searches with VAST Data:** Reducing storage costs via InfiniteCache integration.
* **The Cisco & Splunk Partner Playbook:** Strategies for scaling business in the AI era.
* **Accelerating Your Journey to Agentic Observability:** Navigating 4 steps toward agent-driven monitoring.
* **A Compromised Account Alerting System:** Automating detection of suspicious user behavior.
* **Advanced Threat Detection (DSDL & RBA):** Using machine learning for behavioral risk scoring.
* **Advancing Security Operations with ESCU Innovations:** Leveraging new high-fidelity threat intelligence content.
* **Agentic Data Ingestion (Anthropic's Claude):** Automating Splunk Add-On development using GenAI.
* **Agentic Detection Posture Management:** Using AI to rapidly identify and close detection gaps.
* **Agentic Incident Command:** Deploying coordinated AI agent teams for full incident lifecycles.
* **Hands-on Agent Attack Analysis:** Building anomaly detections from raw agent telemetry.
* **Build Your Own SOC AI Agent (Workshop):** Hands-on guide to building custom agents with guardrails.
* **Triage and Response at Scale:** Using Triage and SOP Agents for automated incident response.
* **Agents Are Easy, Trust Is Hard:** Implementing governance in cohesive agentic operations via Cisco Data Fabric.
* **AI Assistant V2 Unlocked (Workshop):** Hands-on with Agent Mode and organizational knowledge integration.
Ask-search provides a way for AI agents to perform web searches privately and without the need for paid API keys. It works by wrapping SearxNG, which aggregates results from multiple sources such as Google and Bing into a single meta-search engine. This allows local tools like Claude Code or Antigravity to access real-time information while maintaining data privacy.
* Zero cost via self-hosted SearxNG backend
* High compatibility with CLI, MCP servers, and OpenClaw skills
* Flexible search options including language and category filters
* Privacy protection through local query aggregation
* Zero cost via self-hosted SearxNG backend
* High compatibility with CLI, MCP servers, and OpenClaw skills
* Flexible search options including language and category filters
* Privacy protection through local query aggregation
A curated collection of resources, patterns, and templates for building reliable scaffolding for agents. Harness engineering is the discipline of designing the systems surrounding an agent—such as context delivery, tool interfaces, planning artifacts, verification loops, memory systems, and sandboxes—that determine its success or failure on real tasks. This focus is on the harness rather than the model.
- Design primitives for loops, planning, and memory
- Reference implementations and tutorials
- Security, sandboxing, and permissions
- Evaluation, verification, and observability
- Task runners and orchestration
- Human-in-the-loop and production operations
- Design primitives for loops, planning, and memory
- Reference implementations and tutorials
- Security, sandboxing, and permissions
- Evaluation, verification, and observability
- Task runners and orchestration
- Human-in-the-loop and production operations
This tutorial provides a step-by-step guide to building a lightweight personal AI agent inspired by the nanobot architecture in Google Colab. The approach focuses on recreating core components—such as provider abstractions, tool registration, session memory, and lifecycle hooks—rather than relying on heavy external frameworks. Key features include a tool registry for Python functions, token-budgeted memory management, and an MCP-style tool server for external capabilities. The guide includes a complete Python implementation that supports both live OpenAI-compatible models and a deterministic mock provider for offline testing.
Main topics covered:
- Provider abstraction for multi-model compatibility
- Automated tool schema generation using decorators
- Session-specific memory with token budgeting
- Lifecycle hooks for auditing and timing
- Dynamic skill loading and MCP server connection
Main topics covered:
- Provider abstraction for multi-model compatibility
- Automated tool schema generation using decorators
- Session-specific memory with token budgeting
- Lifecycle hooks for auditing and timing
- Dynamic skill loading and MCP server connection
llayer applies the Unix philosophy to large language model orchestration by building framework-free agents with bash, curl, and jq. The architecture decomposes the agent lifecycle into three fundamentals: an append-only JSONL history file for state and memory, a jq stream reducer for context window management, and a standard bash while loop for control flow. This stateless text pipeline enables time-travel debugging via simple file slicing, zero abstraction tooling through native bash functions, and seamless POSIX tool integration for filtering or benchmarking. The system functions as a REPL-style loop that ingests user input, constructs context, evaluates it against a local model like Ollama, handles tool dispatches, and outputs results. All interactions are recorded immutably in a structured JSONL event schema, prioritizing transparency, composability, and minimalist design.
- Append-only JSONL history for auditing and replayability
- Modular command chaining for stateless and stateful interactions
- Docker Compose integration for local Ollama inference
- Transparent POSIX tool pipeline for data filtering and token benchmarking
- Minimalist schema with explicit event types and sources
- Append-only JSONL history for auditing and replayability
- Modular command chaining for stateless and stateful interactions
- Docker Compose integration for local Ollama inference
- Transparent POSIX tool pipeline for data filtering and token benchmarking
- Minimalist schema with explicit event types and sources
An examination of the hype surrounding autonomous AI agent frameworks and why they may add unnecessary complexity to software development. The author argues that for most production use cases, structured workflows using LLM function calling are more reliable than fully autonomous agents.
- Complexity vs control in agentic systems
- Limitations of current models regarding long-term autonomy
- Advantages of explicit programming over unpredictable loops
- Complexity vs control in agentic systems
- Limitations of current models regarding long-term autonomy
- Advantages of explicit programming over unpredictable loops
Google DeepMind has released the Gemma 4 12B, a dense multimodal model featuring an encoder-free architecture. Unlike previous iterations that used separate vision and audio encoders, this model allows these modalities to flow directly into the LLM backbone. This streamlined design reduces latency and memory overhead, allowing the model to perform agentic reasoning tasks on consumer laptops with as little as 16 GB of VRAM while approaching the performance levels of much larger models like the 26B MoE variant.
- Unified decoder-only architecture for text, image, video, and native audio input.
- Encoder-free design using a 35M vision embedder and direct raw audio wave projection.
- Optimized to run locally on Apple Silicon Macs and consumer GPU laptops.
- Released under an Apache 2.0 license with support for llama.cpp, MLX, vLLM, and Ollama.
- Unified decoder-only architecture for text, image, video, and native audio input.
- Encoder-free design using a 35M vision embedder and direct raw audio wave projection.
- Optimized to run locally on Apple Silicon Macs and consumer GPU laptops.
- Released under an Apache 2.0 license with support for llama.cpp, MLX, vLLM, and Ollama.
