Service
Learn to write software effectively with AI coding agents — from solo practice to enterprise team workflows. We recommend OpenCode.
Key capabilities
Overview
Agentic coding is a software development approach where AI agents autonomously handle coding tasks — planning, executing, and iterating with minimal human intervention. The agent observes the codebase, plans changes, executes them, verifies results, and loops until the task is done.
This is not "vibe coding." It is a disciplined engineering practice centred on the agentic loop: gather context → take action → verify → repeat. The developer's role shifts from writing every line of code to orchestrating agents that write it — applying architectural judgment, reviewing diffs, and steering the system toward quality outcomes.
Analyse
Understand the codebase, task, and surrounding context before acting
Plan
Design the approach — files to change, tests to write, risks to avoid
Execute
Edit files, run commands, and iterate with the agent's tools
Verify
Lint, type-check, test, and review — quality gates at every step
Tool landscape
The agentic coding ecosystem has matured rapidly. Here are the tools we work with and teach — each with different strengths depending on your workflow and priorities.
Anthropic's official CLI for agentic coding. Runs in your terminal with full filesystem and command access. Deep Claude model integration with Agent Teams, background scheduling, and computer use.
Our top recommendation. Open-source (MIT) terminal-based agent with 175K+ GitHub stars. Supports 75+ LLM providers, LSP integration, parallel agents, and session sharing. Model-agnostic and privacy-first.
Minimalist, open-source coding agent with a primitives-first design philosophy. Four core tools (read, write, edit, bash), fast execution, and excellent local model support via MLX and GGUF.
OpenAI's agentic coding CLI, deeply integrated with the GPT model family. Strong reasoning capabilities and native tool calling. Best for teams already in the OpenAI ecosystem.
Curriculum
Our training covers the full discipline of agentic coding — from the fundamentals of the agentic loop to advanced multi-agent orchestration and enterprise workflows.
The practice of curating what information the agent has access to, and when. Covers the altitude problem (too specific vs. too vague), just-in-time context retrieval, and the feedback-driven approach to evolving your setup.
AGENTS.md, CLAUDE.md, skills, and project rules. What belongs in each, how to structure them, scoped vs. global rules, and the skill pattern for on-demand expertise loaded only when needed.
Three layers of quality: the agent checking its own work, automated gates (linting, type checking, tests), and human review of the diff. TDD with agents, end-to-end verification, and building a complete quality pipeline.
Delegating tasks to specialised sub-agents for parallel work. Primary vs. sub-agent patterns, agent teams with lead coordination, and when to use each approach for complex multi-file changes.
Safe autonomous execution through containerised isolation, least-privilege permissions, YOLO mode risk assessment, credential management, and blast radius minimisation strategies.
Test your AI agents against prompt injection, tool misuse, and autonomous action risks. We apply adversarial methodologies specifically designed for agentic systems — where unintended actions compound across tool calls and long execution chains.
Choosing between frontier and local models based on task complexity, cost, and privacy requirements. Multi-provider setups, model switching patterns, and when to use each tier of capability.
Our services
We design and deliver hands-on training programmes that take developers and teams from first exposure to confident, production-grade use of AI coding agents.
Understand your team's current workflow, tooling, and skill level. Identify high-impact opportunities for agentic coding adoption.
Set up the agent harness, instruction files, MCP servers, quality gates, and project conventions. Establish the foundation for consistent, safe agent use.
Hands-on training with real codebases. Context engineering, instruction file authoring, verification loop setup, and team workflow design.
Ongoing guidance as the team scales its agentic practice. Advanced topics: multi-agent orchestration, CI/CD integration, and custom sub-agents.
Our recommendation
We recommend OpenCode — the open-source AI coding agent with over 175,000 GitHub stars, released under the MIT license. It is the most popular and most flexible coding agent available in 2026, trusted by over 7.5 million developers every month.
OpenCode is model-agnostic: connect any LLM provider — Claude, GPT, Gemini, DeepSeek, or local models via Ollama — and switch between them freely. Its LSP integration gives the agent compiler-level understanding of your codebase. Parallel agents let you run multiple sessions on the same project. Plan mode provides read-only analysis before any file changes. And because it's open source with zero telemetry, your code never leaves your machine.
175K+
GitHub stars
7.5M
Monthly developers
75+
LLM providers
FAQ
Next step
Book a consultation and we'll design a training programme tailored to your team's workflow, tech stack, and skill level.