AI Architecture LLM

Agentic Context Builder

Turning messy inputs into reliable LLM work units

Industry
Technology
Stack
Python, LLM, Document Processing

Problem

LLM workflows fail when context is inconsistent, too long, or assembled ad-hoc at runtime.

Constraints

Context sources vary wildly; output must be consistent across runs.

Approach

Build a 'context compiler' that normalizes inputs into modular context blocks with strict rules and traceability.

Results

Higher reliability, easier debugging, and reusable context across workflows.

Context

This pattern is for anyone building multi-step agent workflows that need repeatability: claims processing, operations, research pipelines, content workflows.

My role: Designed the architecture and patterns; built working workflows around it.

The Problem

Most agent systems die by a thousand papercuts:

  • Input chaos. Sources come from multiple places in multiple formats.
  • Runtime assembly. Context gets concatenated differently each run—subtle bugs hide everywhere.
  • Token limit roulette. Truncation happens in the worst possible place, silently.
  • Inexplicable outputs. “Why did the model answer that?” becomes unanswerable.

When you can’t trust the context, you can’t trust the output. When you can’t trust the output, you can’t ship.

Key Decisions

  1. Normalize first. Turn raw inputs into a clean intermediate representation (IR) before prompting. Never feed raw documents directly to the model.

  2. Modular context blocks. Separate “facts”, “policy/rules”, “history”, “constraints”, “open questions”. Each block has a purpose.

  3. Traceability built in. Every block has a source and a reason (“why is this included?”). Debugging becomes possible.

  4. Explicit budgeting. Context is assembled with explicit priorities. You know what gets dropped first, and why.

Solution Overview

The Agentic Context Builder produces a Context Pack—a structured bundle of blocks, each with:

  • Name / purpose: What this block is for
  • Content: The actual text
  • Source references: Where this came from
  • Priority: How important is this block
  • Size budget: How much space it can take

A deterministic assembly step produces the final context for each LLM task.

This makes agent workflows:

  • Composable: Reuse the same context pack across steps
  • Debuggable: Inspect individual blocks
  • Safer: Less accidental leakage, less irrelevant noise

What I Built

A repeatable pattern to:

  • Ingest messy inputs (documents, emails, notes, tables)
  • Extract and normalize into structured blocks
  • Build a context pack with clear priorities
  • Assemble task-specific prompts using the pack

Plus:

  • Clear conventions for block naming, priority, and budgets
  • A debugging view concept (“show me the exact context sent to the model”)

Outcome

  • Fewer random failures. Context issues are caught before they cause bad outputs.
  • Faster iteration. Swap blocks, not entire prompts.
  • Better explainability. “This answer came from these blocks” is a real answer.
  • Easier expansion. New workflows reuse the same pattern with different blocks.

What I Learned

Context management is the unsexy core of reliable LLM systems:

  • Quality checks matter. Automated detection of missing fields, contradictions, and stale info would catch problems earlier.
  • Context diffing is valuable. Comparing context between runs explains behavior changes.
  • Metrics unlock optimization. Tracking which blocks correlate with success/failure would guide block design.
  • Block libraries emerge. Common blocks (company policy, user preferences, domain rules) become reusable assets across projects.
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