senn-techCodex, Gemini CLI & Co: The Agent Landscape 2026
The market for agentic coding tools is 2026 astonishingly mature — and diverse. We deliberately use several, instead of committing to a single ecosystem.
The Big Three
Claude Code (Anthropic). Our workhorse: strong in handling large codebases, clean in tool usage (MCP), easily controllable via project rules and skills. When a repo is large and sprawling, Claude Code is usually the first choice. More on this in its own article.
Codex (OpenAI). OpenAI's coding agent, both as a CLI and as a cloud-based agent. Strong in integration into the OpenAI ecosystem and with widely adopted language/framework patterns. Those already moving in that ecosystem find a smooth path here.
Gemini CLI (Google). Google's open-source terminal agent. Interesting due to its openness, connection to Google's models, and large context windows — useful when a lot of code or long documentation must fit into the context at once.
Why Multi-Track
- Diversify strengths: Models are differently skilled across different tasks. Knowing only one tool means seeing only its perspective. What Claude Code dominates, Codex may solve differently — and vice versa.
- Independence: No vendor lock-in or dependency on a pricing model. If an API changes or the price rises, the whole pipeline is not blocked.
- Data sovereignty: For sensitive projects, we pair agents with self-hosted, on-prem models (vLLM, Ollama) — the data stays in-house.
Data Sovereignty as a Hard Factor
For SMEs the last point is often the decisive one. Customer and business data do not leave the house via a coding API. The solution is a hybrid approach: agent control and tool logic stay on our own infrastructure, and only model inference runs on-prem where needed. With vLLM and Ollama this is now realizable without great effort. Cloud-bound agents are fine for non-sensitive work; for anything worth protecting, the agent stays in-house.
How We Choose Per Task
The choice of tool follows the task, not preference. For large, unfamiliar repositories Claude Code is usually the best pick — it keeps the overview and adheres to project rules. For projects tightly embedded in the OpenAI ecosystem, for instance with heavy use of OpenAI services, we reach for Codex. When very large contexts are needed — entire codebases or long technical documents at once — Gemini CLI with its wide window has the edge.
For sensitive undertakings, finally, only the on-prem path is an option, regardless of which front-end drives the agent. The question is not "which tool is best?" but "which tool fits this task and this data?".
The Tool Is Not the Work
All three are impressive. But the common pattern remains: plan, execute, verify — under human supervision. The tool accelerates the craft; it does not replace it. Exactly this craft is our contribution.
Why do you run several coding agents in parallel instead of just one?+
Because strengths scatter — what Claude Code dominates, Codex may solve differently. Several tools also mean independence from a single vendor or pricing model and guard against lock-in. We pick per task: Claude Code for large codebases, Codex inside the OpenAI ecosystem, Gemini CLI when very large contexts are needed.
How do you keep data sovereignty with agentic coding tools?+
For sensitive projects we pair agents with self-hosted, on-prem models (vLLM, Ollama) — the data never leaves the premises. Agent control and tool logic stay on our own infrastructure. We use cloud-bound agents only for non-sensitive work; for anything worth protecting, the agent stays in-house.
Do I need a coding agent at all if my repository is small?+
Not necessarily. The leverage grows with the complexity and sprawl of the codebase. For large, unfamiliar repositories Claude Code keeps the overview and respects project rules. For small, easily surveyed projects, the overhead of multiple agents is not always justified — plan, execute, verify remains human work regardless.