how we built the most advanced ai product planner

#ai #rag #programming #product

intro

𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐈𝐬𝐧’𝐭 𝐚 𝐒𝐢𝐝𝐞𝐰𝐚𝐥𝐤 – 𝐈𝐭’𝐬 𝐚 𝐂𝐢𝐭𝐲

unplanned codebases are like unplanned cities: brittle, inconsistent, and costly to extend.
scalable software, like civil infrastructure, needs clear specs, deliberate architecture, and continuous verification.
skipping these, especially when AI does the typing, leads to rigid, unmaintainable systems.

models as humans reading a book

1️⃣ when you are reading a book, you carry your attention with yourself. you tend to forget irrelevant information and retrieve the important one. this keeps your mind/attention on the track.
2️⃣ but models don't have the same capabilities as human's brain. they go offtrack when fed up everything at once. models do not act phase by phase. models do hallucinations as they do a lot of things at once.
3️⃣ indexing the book before reading, skipping irrelevant chapters & focusing attention on most important words can give you the whole context. this is how we as humans treat our books.

tweaking this same thinking to LLM models, what if we can provide them relevant information & stop telling them everything at once (context bloat).

❓what if we ask our models to do the thing which they are really good at? what if we tell them to do work in phases? what if we spawn an army of agents to do work & collaborate on the tasks after they break down the tasks?

😎 YES, this is exactly like tackling a big problem in smaller sub problems which we proudly call as divide & conquer in computer science's world

become an orchestra not an oracle

assume having a god-model which performs really well when it starts its work but forgets things as it proceeds with the task. that is what an oracle tries to do & hence we see a lot of errors also being generated from a god agent. doing everything at once will drift the agent and going offtrack in code-generation means a lot more iterations.

⚡️that's why we built TraycerAI :- The ultimate AI product planner
the architecture: an ensemble of specialists

instead of a single "god-model" that tries to be a jack-of-all-trades, Traycer operates as a coordinated ensemble. we’ve decoupled intelligence from retrieval, ensuring that our "thinkers" aren't distracted by the "noise" of raw data gathering.

the orchestrator (sonnet-4.5): acts as the conductor. it handles high-level reasoning, complex planning, and task decomposition. it doesn’t get its hands dirty with file searching; it directs the flow.
the critics (GPT-5.1): specializing in code analysis and verification. while one model builds the plan, another, with a different "personality" and training bias, critiques the output to catch regressions.
the scouts (grok-4.1-fast & parallel.ai): these are our high-speed units. they fan out across your codebase and the web in parallel to gather context. they provide the "raw facts" back to the orchestrator without adding their own editorial bias.

defining the loops: outer vs. inner (again)

to understand Traycer, you have to understand where it sits in the development lifecycle. most AI tools are focused on the inner loop, but Traycer owns the outer loop.

the inner loop: the "How"

this is the tactical layer. it’s the act of writing code, patching lines, and running local tests. it’s where your code-gen agents live. it answers the question: "Can you write this specific function for me?"

the outer loop: the "Why" and "What"

Traycer lives here. the outer Loop is the strategic layer that governs the entire change process. it doesn’t just write code; it manages the intent.

strategic planning: before a single line is written, Traycer decomposes a high-level prompt (e.g., "Add rate limiting") into a phased implementation spec.
context synthesis: It determines which files matter across a massive repository, long before a code-gen agent starts its work.
final verification: after the inner loop finishes, the outer loop steps back in to verify the changes against the original architectural constraints.

📍 key takeaway: by separating these loops, Traycer ensures that the "Thinking" (Outer Loop) is never compromised by the "Doing" (Inner Loop). you get the speed of parallel agents with the oversight of a senior architect.

if you are building end to end products, features, you should definitely checkout our EPIC mode in Traycer which:-