Part 2 · Coding agents
Coding agents
& the coding harness
What sits around the model — and why a better harness leads to better code.
The landscape
Some coding agents
The landscape
Some coding agents — what do they cost?
The big picture
Coding agent = model + “coding harness”
MAP
The five building blocks of a coding harness — we’ll walk through each one.
1 · AI model
Coding agent = thin client
The harness picks the model. Swap the brain without changing the tool.
Coding harness
select AI model
→
API key (e.g. Gemini)
Inference provider
Local model (e.g. Gemma 3)
Inference providers are e.g. Nebius, RunPod, Hugging Face Hub, etc.
Coding agent = model + “coding harness”
MAP
2 · Skills & MCP
Give better instructions with Skills
- SKILL.md is an open standard by Anthropic.
- It lets you load a file that instructs the LLM what to do.
- Most often used with Claude…
- …but it’s open source, so it runs with any coding agent.
Find skills at officialskills.sh
---
name: my-pptx-style
description: > Use this skill whenever
creating or editing PowerPoint slides.
Encodes the user's personal style.
---
# My PowerPoint Style Guide
## Design System
— Font: Aptos
— Text: dark navy on white
— Primary #0D2D6B · Accent #1565A7
2 · Skills & MCP
What is MCP?
MCP (Model Context Protocol) is an open-source standard for connecting AI agents with external systems.
Using MCP, agents can connect to data sources, tools and workflows.
Kind of like a specialised API language for agents!
2 · Skills & MCP
MCP vs REST
REST is the right choice when…
- You’re building a service other developers or apps will consume.
- You want standard HTTP tooling — caching, auth, rate limiting, load balancing.
- The consumer just needs data, not reasoning.
MCP is best when…
- An LLM needs to decide which tool to call from a natural-language request.
- You want one agent to expose capabilities to another agent.
- The set of available tools isn’t known at code-write time.
Coding agent = model + “coding harness”
MAP
3 · Prompt & context engineering
Prompt & context engineering
Prompt engineering: write better & longer prompts.
Context engineering: give the model the right context documents:
- Architecture
- Style guides
- Patterns
- Master data
Coding agent = model + “coding harness”
MAP
4 · Memory
Three tiers of memory
The coding harness uses three distinct kinds of “memory” — only one of them actually learns.
🔁
Session state
Short-term · local
- Everything from the current session.
📝
Instructions
Durable · authored
- AGENTS.md and .github/instructions/
- Used from Skills
- Deterministic config, not learned
- Settings as SDD / TDD
🧠
Memory
Learned · hosted
- Captured automatically as it works
- Repo facts + user preferences
- Validated; auto-expires after 28 days
- Shared across CLI, cloud agent, review
Coding agent = model + “coding harness”
MAP
5 · Sub-agents
Multi-agent systems
Example: a city-noise report pipeline — three agents, one task.
Task: “Measure background noise at 5 Amsterdam intersections and produce an ISO 9614 compliance report.”
1
Planner
Generates high-level tasks
Responsibilities
- Decompose goal into subtasks
- Assign locations & priorities
- Set success criteria per task
- Sequence tasks & timing
Output: ordered task list with criteria
2
Worker
Executes the tasks
Responsibilities
- Call measurement API per location
- Apply background-subtraction formula
- Retrieve noise ontology via MCP
- Write sections of the ISO report
Output: measurements + draft report
3
Verifier
Controls every result
Responsibilities
- Check margin ≥ 3 dB per measurement
- Validate ISO 9614 format compliance
- Flag uncertain results for re-run
- Approve or reject each section
Output: verified report or rejection + reason
Wrap-up
Summary
We discussed the five most important elements of a “coding harness”:
AI model
Skills.md & MCP
Prompt & context engineering
Memory
Sub-agents
This presentation was just a first introduction to the subject.
Better coding harnesses will lead to better code. In the second half of 2026 we’ll see lots of improvements in coding harnesses. Watch this space.