The state machine framing is useful. Understanding coding agents as state machines that transition between planning, executing, and verifying helps explain why some agent setups work and others don't. What I've found in practice is that the verification state is where most implementations fall short. My agent has a strict rule: never mark a task complete without proving it works. Run the code, check the output, test edge cases, show proof. That single constraint improved reliability more than any model upgrade. The MCP section is also relevant. Having standardized tool integration means agents can extend their own capabilities systematically. I explored this architecture in depth: https://thoughts.jock.pl/p/ai-agent-self-extending-self-fixing-wiz-rebuild-technical-deep-dive-2026
Nice breakdown! One concept I expected to gain traction but hasn't is meta-prompting - dynamically enriching prompts with contextual instructions before executing them, rather than relying on static generic rules or late compensation by other tools. For example, we use it in Apiiro to weave contextual security guidance into coding prompts of our customers, resulting in very secure code generation. Are you familiar with any notable use cases? Do you see this approach becoming more prevalent?
In some way, it's another extension of context management. Basically, query the security information that you need or query the info about the package that you want to use.
The triggering problem is a massive issue here for the security use case, hooks are triggering too much (and don't have standardization currently), and other triggers like skills are not triggered when they should in a relaible way
Great breakdown, the internal concept we are taking for granted here is the loop. It’s not just the reasoning, the tools and the context. It’s their evolvement in each turn.
Fantastic breakdown of what's actually happening under the hood. The framing of the LLM as "powerful CPU with massive RAM but no hard drive" cuts through so much marketing fluff. I've been working with Cursor lately and the layered context management makes way more sense now, especially that needle-in-haystack risk with massive context windwos. The standardization angle is the real unlock here.
The state machine framing is useful. Understanding coding agents as state machines that transition between planning, executing, and verifying helps explain why some agent setups work and others don't. What I've found in practice is that the verification state is where most implementations fall short. My agent has a strict rule: never mark a task complete without proving it works. Run the code, check the output, test edge cases, show proof. That single constraint improved reliability more than any model upgrade. The MCP section is also relevant. Having standardized tool integration means agents can extend their own capabilities systematically. I explored this architecture in depth: https://thoughts.jock.pl/p/ai-agent-self-extending-self-fixing-wiz-rebuild-technical-deep-dive-2026
Nice breakdown! One concept I expected to gain traction but hasn't is meta-prompting - dynamically enriching prompts with contextual instructions before executing them, rather than relying on static generic rules or late compensation by other tools. For example, we use it in Apiiro to weave contextual security guidance into coding prompts of our customers, resulting in very secure code generation. Are you familiar with any notable use cases? Do you see this approach becoming more prevalent?
In some way, it's another extension of context management. Basically, query the security information that you need or query the info about the package that you want to use.
The triggering problem is a massive issue here for the security use case, hooks are triggering too much (and don't have standardization currently), and other triggers like skills are not triggered when they should in a relaible way
Great breakdown, the internal concept we are taking for granted here is the loop. It’s not just the reasoning, the tools and the context. It’s their evolvement in each turn.
Fantastic breakdown of what's actually happening under the hood. The framing of the LLM as "powerful CPU with massive RAM but no hard drive" cuts through so much marketing fluff. I've been working with Cursor lately and the layered context management makes way more sense now, especially that needle-in-haystack risk with massive context windwos. The standardization angle is the real unlock here.