Configuration Patterns in the TI C2000 (F29H85x)

This is something I learned during my internship at Texas Instruments, while working on a project building an agentic interface for configuring the part.

The F29H85x is a real-time MCU (C2000 family), which means it has to be programmed in register-level C — there’s no abstracting that away. TI ships SysConfig to help with peripheral configuration, but even with that add-on in the IDE, getting a configuration right is still difficult. The reason is that the dependencies between configuration fields — clock trees, pin muxing, EPWM/ADC/SAR interlocks, order-of-operations constraints — are largely hidden. Technically, they’re documented — but scattered across the Technical Reference Manual, which runs thousands of pages — so in practice they’re not surfaced by SysConfig’s UI, and users (and agents) end up discovering them the hard way, by trial and error.

The approach I’ve been taking is to parse the SDK’s own metadata — the same data SysConfig itself is built on — to extract those dependency relationships programmatically, rather than relying on them being documented anywhere readable. Once extracted, they can be exposed as an explicit dependency graph, which is useful both for a person configuring the part by hand and for an agent trying to generate a valid configuration automatically.

To make sense of it, I built a couple of interactive dependency-graph tools that map these relationships visually:

What I’m looking at

The dependency patterns fall into a few recurring categories:

• Clock tree dependencies — a peripheral’s valid configuration range shifts depending on upstream clock source and divider settings, which aren’t always local to that peripheral’s own config.
• Pin muxing constraints — which peripheral can be routed to which pin depends on what else is already claiming that pin or an adjacent one.
• EPWM/ADC/SAR interlocks — triggering and timing settings on one peripheral silently constrain valid settings on another.
• Order-of-operations traps — some registers have to be written in a specific sequence relative to others, or the configuration takes effect incorrectly (or not at all) with no error raised.

None of these are hard to reason about individually — the difficulty is that they’re scattered across the SDK and the Technical Reference Manual rather than collected anywhere as a single, explicit graph.

Why it matters

For people

This means configuration by trial and error: build, test on hardware, hit a subtle bug, and trace it back to a dependency that was never documented anywhere convenient. The knowledge exists, but only as tribal knowledge or a needle in the Technical Reference Manual’s haystack.

For AI agents

For an agent generating configuration code, these cross-configuration dependency chains are a chicken-and-egg problem. An agent can’t know which dependencies matter until it has already explored the configuration space, but exploring that space safely requires already knowing the dependencies. Lacking an explicit map, the agent’s only recourse is to dig through source code examples and SDK internals hunting for precedent — expensive, slow, and still no guarantee it catches every hidden interlock. It will often generate a configuration that looks correct and fails silently on real hardware.

A parsed dependency graph sidesteps that loop entirely. Instead of an agent having to rediscover the constraint space by trawling examples, the lightweight configuration-pattern map — extracted once from SDK metadata — gives it (and a person) something to check a candidate configuration against directly, before it’s ever flashed to hardware.