Advanced Context Engineering: The Technical Craft Behind Effective AI Coding

Introduction: Beyond the Hype

If you've spent any time with AI coding tools, you've probably experienced both extremes: the magical moment when Claude ships a perfect PR in minutes, and the frustrating afternoon watching it spin in circles, burning tokens while producing garbage.

The difference isn't the model's mood. It's context engineering.

At FMKTech, we've helped organizations transform AI coding from an expensive experiment into a production capability. The secret isn't better prompts or more powerful models—it's treating context engineering as the deeply technical craft it actually is.

In January 2025, a team using advanced context engineering techniques fixed a bug in a 300,000-line Rust codebase they'd never seen before—in under an hour. A few weeks later, the same team shipped 35,000 lines of code adding cancellation and WASM support to the same project in just seven hours. These weren't simple CRUD apps or Next.js prototypes. This was complex systems code in an unfamiliar language and codebase.

How? They understood something fundamental: AI coding agents are stateless functions where context is the only input variable you control.

This isn't another "10 prompt tips" article. This is about treating context engineering as the deeply technical craft it actually is—one that determines whether your team ships production code or burns money on hallucinations.

What Actually Is Context Engineering?

Context engineering is the practice of deliberately designing what information goes into an AI model's context window, when, and in what form. It's the difference between:

Naive approach:

You: "Fix the authentication bug"
[AI searches 50 files, fills context with noise, produces broken code]

Engineered approach:

1. Research phase: Spawn specialized agents to locate relevant files
2. Compact findings: Distill 50 files into 200 lines of precise references
3. Implementation: Fresh context with only what's needed to fix the bug
[AI fixes the bug correctly on first try]

Think of it like this: if the AI is a stateless function, then the quality of your output is entirely determined by the quality of your input. There is no training, no fine-tuning you can do mid-session—the context window is your only lever.

It's like cooking with a sous chef who has perfect knife skills and flawless technique, but amnesia that resets every conversation. You can't teach them—you can only give them the perfect recipe each time. Context engineering is the art of writing those perfect recipes.

The Core Problem: Context Window Degradation

Every LLM interaction sends the entire conversation history back to the model. This creates a fundamental problem that most people don't understand:

The Context Utilization Curve

At 0-40% context utilization, models perform well. They have room to think, can focus on what matters.

At 40-60%, performance is still good but starts declining. This is the sweet spot for complex work.

At 60-80%, you enter what practitioners call "the dumb zone"—the model struggles to focus on what actually matters amid all the noise. Think of it as trying to have a conversation at a crowded party. At 40% volume, you can hear perfectly. At 80%, you're shouting and still missing half of what's being said.

At 80%+, performance degrades rapidly. The model loses track of objectives, hallucinates, and produces inconsistent results.

What Fills Your Context?

Let me show you how quickly this spirals. You ask Claude to "add user authentication":

After 5 minutes: 15% context used

• 12 file searches looking for existing auth patterns
• 8 file reads examining authentication code
• 3 grep operations finding config files

After 15 minutes: 45% context used

• Previous searches still in context
• 6 more file reads after finding related files
• 2 failed implementation attempts with full diffs
• Build output from test failures (2,000 lines of Jest output)

After 30 minutes: 72% context used

• All previous context still present
• Another round of searches to fix the failing tests
• More failed attempts
• JSON responses from package.json, tsconfig.json, environment configs

After 45 minutes: 89% context used

• You're now in the "dumb zone"
• The model is re-reading files it already processed
• Suggesting solutions it already tried
• Missing obvious issues because it can't track what matters
• You're basically arguing with someone who's had too much coffee and not enough sleep

That's the invisible killer. Each interaction looks innocent—"just one more file read"—but the cumulative weight crushes the model's ability to reason. The items filling your context aren't individually problematic:

• Tool calls and responses: Every file search, every grep, every read operation
• Code edits: The full diff of every change
• Build logs: Test output, compilation errors, linting warnings
• JSON blobs: Response from MCP tools, API calls, configuration files
• Failed attempts: Dead ends and wrong turns that didn't work

In a naive workflow, you burn through 60-70% of your context just searching for the right files. By the time you're ready to implement, the model is already struggling, and you haven't even written the feature yet.

The Solution: Frequent Intentional Compaction

The breakthrough technique is called Frequent Intentional Compaction (FIC)—designing your entire workflow around keeping context utilization in the 40-60% range through strategic compression.

If you've read our article on the Ralph Wiggum technique, you've already seen one form of compaction: the infinite loop that resets context between tasks. FIC extends this principle to interactive development workflows where you need to maintain continuity across complex, multi-step tasks.

Three Types of Compaction

Context compaction isn't one technique—it's three distinct approaches suited to different situations. Think of them as gears in a transmission: ad-hoc compaction for quick tactical resets, subagent-based compaction for isolating messy research, and workflow-based compaction for systematic process design. The key is knowing which gear to use when.

1. Ad-Hoc Compaction

When you notice context filling up, explicitly compress it:

You: "Write everything we've discovered about the authentication flow
to research.md. Include:
- The relevant files (path:line format)
- How the data flows between components
- The root cause of the bug we're investigating
- Current status and next steps"

[Start fresh session]

You: "Read research.md and implement the fix"

The key insight: you're compressing 50-100 tool calls and responses into a 200-line markdown file that captures everything that matters.

2. Subagent-Based Compaction

Delegate research to specialized agents that work in isolated context windows:

Main agent: "I need to understand the authentication flow"
[Spawns research subagent with fresh context]

Research subagent:
- Searches for auth-related files
- Reads relevant code
- Traces data flow
- Returns compact summary

Main agent receives:
"Authentication flow:
- Entry: src/auth/handler.ts:45
- Validation: src/auth/validator.ts:123
- Token generation: src/auth/jwt.ts:89
- Error handling: src/auth/errors.ts:34"

[All the search noise stays in subagent's context, never pollutes main thread]

3. Workflow-Based Compaction

The most powerful approach: design your entire development process as a series of compaction steps.

The Research → Plan → Implement Workflow

This is where context engineering becomes a full team discipline. Instead of letting context accumulate organically, you structure work into distinct phases, each with its own context budget.

Phase 1: Research

Goal: Understand the codebase and gather relevant information Context Budget: 40-60% for research findings Output: Structured markdown with file references

Process:

1. Spawn parallel research subagents to find relevant code
2. Each subagent works in isolated context, returns compact findings
3. Synthesize into a research document with specific file:line references
4. Human reviews research before proceeding

Example research output:

# Authentication Bug Research

## Current Implementation
- User login handled in `src/auth/handler.ts:45-89`
- JWT validation uses custom logic at `src/auth/jwt.ts:123`
- Session storage in `src/store/session.ts:67`

## Bug Location
The validation bypass occurs in `src/auth/jwt.ts:156` where expired
tokens aren't properly rejected when the grace period check fails.

## Fix Required
Update token validation logic to properly handle expired tokens.
Test coverage exists at `tests/auth/jwt.test.ts:234`

Critical insight: A bad line of research can lead to thousands of bad lines of code. This is the highest-leverage point for human attention.

Think of it this way: if research is your foundation, you don't want to build a skyscraper on quicksand. Ten minutes verifying the foundation saves weeks of structural collapse later.

Phase 2: Planning

Goal: Create detailed implementation steps Context Budget: 30-40% for the plan Output: Phase-by-phase implementation guide with success criteria

Process:

1. Read research document (compact!)
2. Optionally spawn more focused subagents for deeper investigation
3. Design implementation approach
4. Break into testable phases
5. Human reviews and approves plan before implementation

Example plan structure:

# Fix JWT Validation Bug

## Phase 1: Add Failing Test
Changes: tests/auth/jwt.test.ts
- Add test case for expired token with grace period edge case

Success Criteria:
- Automated: Test fails with current code
- Manual: Verify test captures the exact bug scenario

## Phase 2: Fix Validation Logic
Changes: src/auth/jwt.ts:150-170
- Update isTokenValid() to properly check expiry
- Ensure grace period logic is consistent

Success Criteria:
- Automated: All tests pass, including new test
- Automated: No linting errors
- Manual: Verify fix handles edge cases correctly

## Phase 3: Add Integration Test
Changes: tests/integration/auth.test.ts
- Add end-to-end test for auth flow with expired tokens

Success Criteria:
- Automated: Integration tests pass
- Manual: Test against staging environment

Critical insight: A bad line in a plan leads to hundreds of bad lines of code. This is your second-highest leverage review point.

The planning phase is where you translate understanding into action. Get it right, and implementation becomes almost mechanical. Get it wrong, and you're sending the AI on a wild goose chase with expensive consequences.

Phase 3: Implementation

Goal: Execute the plan with minimal context waste Context Budget: Varies by phase complexity Output: Working, tested code

Process:

1. Start fresh session with plan document
2. Implement phase 1
3. Run automated verification
4. Human verifies manual criteria
5. Compact progress into plan if context is filling
6. Move to next phase

The magic: Because research and planning happened in separate contexts, the implementation agent starts with a clean slate and only the essential information needed to execute.

Real-World Example: Fixing a Rust Bug in an Unfamiliar Codebase

Let's look at how this workflow performed on a real task: fixing a bug in BAML, a 300,000-line Rust codebase for a programming language. The developer had never touched this codebase before and was an amateur Rust developer.

The Bug

Test assertions with a single @ were being silently ignored:

function HelloWorld {
  test {
    functions [SayHello]
    args {
      name "World"
    }
    @assert({{ _.equals("Hello Fu") }})  # Silently passes when it should fail!
  }
}

Research Phase (20 minutes)

First attempt produced research claiming the bug didn't exist. The developer read the research, recognized it was wrong, and restarted with better steering.

Second attempt produced:

# BAML Test Assertions Research

## Issue Location
The parser accepts both `@assert` and `@@assert` syntax in test blocks
(parser/src/ast/expression.rs:89), but validation only checks for `@@assert`
(validation/src/test_case.rs:234).

## Files to Modify
- validation/src/test_case.rs:234 - Add field attribute validation
- tests/validation/test_assertions.rs - Add test cases

## Implementation Approach
Add validation in visit_test_case() to reject single @ assertions
with a clear error message directing users to use @@.

Lesson: Even research fails sometimes. Read it. Verify it. Iterate if needed. This is high-leverage human time.

Planning Phase (15 minutes)

With research in hand, planning produced:

# Implementation Plan

## Phase 1: Add Validation Test
File: tests/validation/test_assertions.rs
Add test case that should fail on single @ assertions

## Phase 2: Implement Validation
File: validation/src/test_case.rs:234
In visit_test_case(), add check for field attributes and return
validation error with helpful message

## Phase 3: Verify Fix
- Run validation tests
- Verify error message is clear
- Test with example from bug report

Implementation Phase (25 minutes)

Starting with a fresh context containing only the plan and research references, implementation proceeded cleanly. The resulting PR was approved the next morning by a maintainer who didn't even know this was an experiment.

Total time: ~1 hour Context efficiency: Each phase used 40-50% of its context window Result: Production-quality code in unfamiliar codebase

The Time and Cost Mathematics

Let's make this concrete with real numbers from the BAML bug fix.

Traditional approach (no AI):

• Learn Rust fundamentals: 40 hours
• Understand BAML architecture: 60 hours
• Find bug location: 8 hours
• Implement fix: 4 hours
• Total: 112 hours at $150/hr = $16,800

Naive AI approach (no context engineering):

• Ask Claude to fix the bug: 15 minutes (getting context)
• Watch it search randomly: 45 minutes (60+ file reads, mostly wrong)
• Implement wrong solution: 30 minutes (based on bad research)
• Debug why tests fail: 90 minutes (context too polluted to help)
• Start over with better prompt: 120 minutes (repeat cycle)
• Finally get working fix: 60 minutes (after third attempt)
• Total: ~6 hours at $150/hr = $900
• AI costs: ~800K tokens at $3/million input, $15/million output ≈ $8
• Combined: $908

Context-engineered approach (actual results):

• Research phase: 20 minutes (focused, compacted findings)
• Planning phase: 15 minutes (clear implementation steps)
• Implementation: 25 minutes (clean context, worked first time)
• Total: 1 hour at $150/hr = $150
• AI costs: ~200K tokens at $3/million input, $15/million output ≈ $2.10
• Combined: $152.10

The naive approach is 6x faster than traditional (saving $15,892), but the context-engineered approach is 6x faster than naive (additional $755.90 savings) while producing higher-quality code that passed review on first submission.

This isn't a cherry-picked example. It's the pattern that emerges when you treat context engineering as a discipline:

• Traditional: Slow but thorough, expensive expertise required
• Naive AI: Faster but chaotic, burns time in failed attempts
• Context-engineered AI: Fastest AND highest quality, systematic process

The gap only widens as tasks get more complex. The 35,000-line WASM feature mentioned earlier? That would have been 400+ hours of traditional development ($60,000). Naive AI might have gotten there in 80-120 hours ($12,000-$18,000) with significant rework. Context engineering: 7 hours ($1,050) with production-quality output.

Advanced Techniques

Parallel Research with Subagents

For complex features, spawn multiple research subagents simultaneously:

[Spawn these in parallel]
- Research database schema patterns
- Research API endpoint conventions
- Research testing patterns
- Research error handling approach

[Each works in isolation, returns compact findings]
[Synthesize all findings into single research doc]

Benefit: Faster research, each subagent stays focused, no context pollution

Progressive Compaction During Implementation

For multi-phase work, compact progress back into the plan between phases:

## Phase 1: Database Migration ✅
[Original plan details]

**Completed**: Migration created at db/migrations/001_add_auth.sql
**Issues encountered**: None
**Next phase dependencies**: Migration must run before Phase 2

## Phase 2: Add Store Methods [IN PROGRESS]
[Implementation continues with compact context]

Test-Driven Compaction

The most reliable pattern discovered: always write tests first.

## Phase 1: Write Failing Test
- Write test that captures desired behavior
- Verify it fails

## Phase 2: Implement Feature
- Make the test pass
- Verify all other tests still pass

## Phase 3: Refactor
- Clean up implementation
- Ensure tests remain green

Why this works: Tests are natural compaction. They specify behavior precisely without implementation details, giving the model clear success criteria without burning context.

The Human-in-the-Loop Paradox

Here's what separates successful AI coding from expensive experiments:

You must read the research and plans.

This seems obvious but is violated constantly. People generate research and immediately feed it to planning without reading. They approve plans they skimmed. They let implementation run unattended for hours, checking back only when something breaks or the context window fills up.

The breakthrough teams do the opposite. They treat each phase transition as a decision point requiring human judgment.

Research Review (5-10 minutes)

When the research phase completes, read it. Actually read it. Ask yourself: Does this correctly identify the relevant code, or did the agent wander into the wrong modules? Are critical files missing from the analysis? Is the root cause diagnosis sound, or is it treating symptoms? If the foundation is wrong, restart with better steering now—not three hours into implementation when you discover the agent was solving the wrong problem.

Plan Review (10-15 minutes)

Before approving an implementation plan, scrutinize the phases. Are they properly scoped, or will phase 2 balloon into a six-hour odyssey? Will this actually solve the problem you identified in research? Are the success criteria specific enough that you'll know when you're done, or are they vague aspirations like "improve performance"? What edge cases are missing from the plan that will bite you during implementation?

This isn't busywork—it's leverage. A bad assumption in the plan cascades into hours of wasted implementation.

Implementation Monitoring (periodic check-ins)

During implementation, check in periodically. Is the agent following the plan or has it wandered off into refactoring the entire module? Are tests passing, or is it stuck in a failing test loop, making the same fix repeatedly? Is it making progress, or burning tokens in circles?

The math: Spending 30 minutes reviewing research and plans often saves 3+ hours of implementation spinning in circles.

This is similar to the brownfield development approach we discuss in our AI coding in brownfield projects article—systematic research before implementation prevents expensive mistakes downstream.

Why This Works: The Leverage Pyramid

Not all lines are created equal:

• A bad line of code: A bad line of code
• A bad line in an implementation plan: 10-100 bad lines of code
• A bad line of research: 1,000-10,000 bad lines of code
• A bad workflow design: 100,000+ bad lines of code

Therefore: Focus human attention at the highest leverage points.

Traditional code review focuses on the lowest-leverage point (reviewing code after it's written). Context-engineered workflows focus on the highest-leverage points (research and planning).

Common Failure Modes

Every failure mode has the same root cause: ignoring the context budget. It's like maxing out your credit card—each small charge seems fine, but the compound effect crushes you.

1. The "Keep Going" Trap

Symptom: Context at 75%, you think "just a bit more"

What happens: The model starts suggesting solutions it already tried. It re-reads files it just examined. It loses track of what you asked for. You spend the next hour watching it produce increasingly confused output.

What this costs: That "just a bit more" decision burns 2-3 hours as you watch the model spiral, then another hour cleaning up the broken code it produced. On a $200/hr developer, that's $600-$800 for ignoring a 60% threshold.

Fix: Hard stop at 60%, compact before continuing. Five minutes to write progress to markdown saves three hours of degraded performance.

2. The Magic Prompt Syndrome

Symptom: Constantly tweaking prompts hoping for better results

What happens: You spend 20 minutes rephrasing the same request. "Fix the auth bug" becomes "Please fix the authentication bug in the login flow" becomes "I need you to debug and fix the authentication issue in src/auth/..." The problem isn't your phrasing—it's that the model's context is full of noise.

It's like trying to fix a car by describing the problem in different languages. The mechanic doesn't need better vocabulary—they need a clear view of the engine.

What this costs: A junior engineer doing this wastes 2-3 hours per day on prompt tweaking. That's 10-15 hours per week, 40-60 hours per month. At $100/hr, that's $4,000-$6,000 per month in pure waste.

Fix: Stop optimizing prompts. Fix your workflow. The same "Fix the auth bug" prompt works perfectly when context is clean and fails miserably when context is polluted.

3. The Research-Free Implementation

Symptom: "This looks simple, let's just implement"

What happens: Without research, the model doesn't know where the relevant code lives. It searches randomly. Reads the wrong files. Fills context with noise. Then implements a solution that breaks existing functionality because it didn't know about the dependencies.

What this costs: You think you're saving 10 minutes by skipping research. Instead, you spend 90 minutes debugging why the "simple" change broke three other features. Then another 60 minutes in code review explaining why you didn't check for existing patterns.

Fix: Even "simple" tasks get a 5-minute research phase. That 5-minute investment prevents the 2-hour debugging session.

4. The Unchecked Research

Symptom: Generate research, immediately feed to planning

What happens: The research is wrong. It identified the wrong files, misunderstood the architecture, or missed a critical dependency. Now planning creates a detailed implementation plan based on false assumptions. Implementation executes that plan perfectly, producing perfectly wrong code.

What this costs: Bad research leads to bad plans leads to bad implementation. You've now spent 3-4 hours going in completely the wrong direction. Worse, the code looks reasonable, so it gets merged, and the bug appears in production three weeks later.

Fix: Always read research before proceeding. Ten minutes of human review catches the 90% of research errors that lead to hours of wasted implementation.

5. The Missing Compaction

Symptom: One long conversation accumulating context

What happens: You're in hour three of a session. Context is at 85%. The model is suggesting solutions you rejected an hour ago. It's importing packages you explicitly said not to use. You think you're making progress, but you're actually burning money on a model that's too overwhelmed to help.

What this costs: A 4-hour uncompacted session might produce 8-10 hours of rework. At senior engineer rates ($150-200/hr), that's $1,200-$2,000 in waste from a single marathon session. Teams doing this daily burn $5,000-$10,000/week.

Fix: Compact proactively, don't wait for problems. When context hits 50%, take five minutes to compress. It's the difference between shipping features and burning budgets.

Measuring Success

How do you know if your context engineering actually works? Not by vibes or occasional wins, but by systematic improvement across measurable dimensions.

Context Utilization: The 40-60% Sweet Spot

What to measure: Context usage at key decision points—end of research, end of planning, before each implementation phase.

What good looks like: Consistently staying in 40-60% range. Not because you're doing less work, but because you're compacting aggressively.

Why it matters: Every percentage point above 60% decreases model reasoning ability. The difference between 55% and 75% context usage isn't subtle—it's the difference between shipping correct code and burning an afternoon on hallucinated solutions.

How to track it: Most AI coding tools show context usage. If yours doesn't, ask. If you're regularly hitting 70%+, you're working too hard, not too little. You need more frequent compaction.

Tool Efficiency: Fewer Calls, Better Results

What to measure: Number of tool calls (file reads, searches, greps) per successful code change.

What good looks like: With engineered context, fixing a bug might take 15-20 tool calls. Without it, the same bug takes 60-80 calls as the model searches randomly and re-reads files.

Why it matters: Tool calls aren't just numbers—they're noise accumulating in context. More calls = more context pollution = worse performance. Efficient research that finds the right files quickly keeps context clean.

How to track it: Count tool calls in successful vs failed sessions. If you're seeing 50+ tool calls for simple changes, your research phase isn't working.

First-Attempt Success Rate: Right the First Time

What to measure: Percentage of implementations that work correctly on first try (tests pass, requirements met, no obvious bugs).

What good looks like: With proper context engineering, 60-80% of implementations should work on the first attempt. You're not getting lucky—you're giving the model the precise context it needs to succeed.

Why it matters: Every failed attempt adds noise to context. The edit diff, the error output, the debugging attempts—all of it pollutes the context window. High first-attempt success means clean context, focused model attention, and compound productivity gains.

How to track it: Log whether implementations succeed on first try. If you're below 40%, your planning phase probably isn't detailed enough or your research missed critical context.

Token Economics: Doing More with Less

What to measure: Total tokens used per feature shipped, per bug fixed, per PR merged.

What good looks like: Context engineering should reduce token usage by 40-60% while increasing output quality. You're not processing less information—you're processing it more efficiently through compaction.

Why it matters: Tokens cost money, but more importantly, they represent cognitive load. High token usage for simple tasks means you're making the model work too hard to filter signal from noise. Efficient token usage means clean context.

How to track it: If your AI provider charges per token, you already have this data. Watch for patterns—do simple features consume as many tokens as complex ones? That's a warning sign.

Code Review: The Quality Filter

What to measure: How much code changes between "implementation complete" and "approved PR."

What good looks like: Code passes review with minimal changes—maybe formatting tweaks, a naming suggestion, edge case addition. Not architectural rewrites or bug fixes. When research and planning are solid, implementations should be near-perfect.

Why it matters: Major review changes mean the implementation didn't match requirements. That traces back to either bad research (identified wrong patterns) or bad planning (designed wrong solution). Review feedback tells you which phase failed.

How to track it: Calculate percentage of lines changed in review. Above 20%? Your context engineering has upstream problems.

Implementation-Plan Alignment: Staying on Track

What to measure: Did the implementation follow the plan? Or did it diverge into unplanned changes?

What good looks like: Implementation should execute the plan with minimal surprises. When you check git diff against the plan, they should match closely. Deviations should be rare and well-justified.

Why it matters: Divergence from plan means either the plan was wrong (bad research or planning phase) or the implementation got lost (context too full or plan not specific enough). Either way, it's a context engineering failure.

How to track it: During code review, compare commits to the original plan. Are you implementing what you planned? Or discovering problems mid-implementation?

Test Coverage: Quality at Scale

What to measure: Are tests comprehensive? Do they cover edge cases? Do they actually validate the requirements?

What good looks like: Tests should feel like they were written by someone who deeply understands the feature. Because they were—by a model with clean context and clear success criteria.

Why it matters: Bad tests reveal unclear requirements or polluted context during planning. Good tests prove the model understood the feature completely. Test quality is a lagging indicator of context quality.

How to track it: Review test coverage percentages, but more importantly, review test scenarios. Are they meaningful? Do they catch actual bugs? Or just achieve coverage numbers?

Team Alignment: Shared Mental Models

What to measure: How much discussion is needed to understand a change? How often do team members ask "why did we do it this way?"

What good looks like: Research and plan documents answer questions before they're asked. New team members can read research to understand decisions. Six months later, you can read your own plan and remember why you made specific choices.

Why it matters: Context-engineered workflows produce artifacts (research.md, plan.md) that preserve understanding. Traditional workflows produce only code, losing the reasoning. Team alignment is the ultimate measure of whether your context engineering creates durable understanding.

How to track it: Count questions in code reviews and team discussions. High questions = context wasn't captured in artifacts.

The Meta-Metric: Velocity × Quality

The ultimate measure isn't any single metric—it's the combination. Good context engineering should increase both velocity (features shipped per week) AND quality (bugs in production, code review iterations).

What good looks like:

• 2-3x more features shipped per engineer
• 50% fewer bugs reaching production
• 40% less time in code review cycles
• Engineers actually enjoying working with AI instead of fighting it

Why it matters: Anyone can ship fast with low quality or slow with high quality. Context engineering lets you have both.

How to track it: Compare month-over-month metrics. As your context engineering matures, you should see compound improvements—not just faster shipping, but higher-quality shipping with less rework.

The Future: Specs as Code

The logical endpoint of advanced context engineering is a fundamental shift in what we consider "source code."

Today: Code is source, specs are documentation Tomorrow: Specs are source, code is compiled output

Just as you wouldn't check in a compiled .jar file and throw away the Java source, we're moving toward a world where checking in code without the research and plans that generated it is similarly wasteful.

The teams already working this way treat their thoughts/ directories (where research and plans live) as more important than their src/ directories. The code can be regenerated. The understanding captured in research cannot.

This isn't a distant future—it's happening now. Organizations that master context engineering are building institutional knowledge faster than ever before, captured not in tribal lore or Slack threads, but in structured, versioned, searchable research documents that compound in value over time.

Practical Starting Point

Want to try this? Start here:

Level 1: Basic Compaction

1. When context hits 50%, stop
2. Ask AI to write current progress to a markdown file
3. Start fresh session, reference that file
4. Continue work

Level 2: Use Subagents

1. For any "find" or "research" task, use a subagent
2. Let it work in isolation
3. Get compact results back
4. Keep main context clean

Level 3: Three-Phase Workflow

1. Research phase: What needs to change and why
2. Planning phase: How to change it, step by step
3. Implementation: Execute the plan
4. Review research and plans, not just code

Level 4: Team Process

1. Standardize research/plan templates
2. Make research and plan review mandatory
3. Check research/plans into version control
4. Use them for onboarding and knowledge sharing

Conclusion: Engineering, Not Magic

The difference between teams shipping production code with AI and teams producing expensive garbage isn't the models they use. It's whether they treat context engineering as a serious technical discipline.

Advanced context engineering is:

• Understanding context as your only control variable
• Designing workflows that keep context utilization optimal
• Focusing human review at the highest-leverage points
• Treating research and plans as first-class artifacts

It's not magic. It's engineering. And like all engineering disciplines, it rewards study, practice, and rigor.

The teams that master this are already shipping 10x more code than their peers. Not because they found a magic prompt, but because they built systematic processes that maximize the quality of context going into every AI interaction.

In the AI-assisted future, the best engineers won't be the ones who write the most code. They'll be the ones who engineer the best context.

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Ready to Transform Your AI Development Workflow?

At FMKTech, we specialize in helping organizations implement production-ready context engineering practices. Whether you're struggling with AI coding agents that spin in circles, burning through token budgets without shipping code, or trying to scale AI development beyond experimental pilots, we can help.

Our team brings deep expertise in:

• Context engineering workflows that keep AI agents in the "smart zone"
• Research-Plan-Implement processes for complex codebases
• Systematic quality controls that catch errors before they become expensive
• Team training and adoption to build organizational capability
• Metrics and measurement to prove ROI and identify bottlenecks

We don't just consult—we implement, iterate, and optimize alongside your team until AI coding becomes a competitive advantage rather than an expensive experiment.

Contact us to discuss how context engineering can transform your AI development velocity while maintaining the quality your business requires. Let's turn these techniques into measurable results for your organization.

Further Reading

FMKTech Articles

• The Ralph Wiggum Revolution: How infinite loops and context resets enable overnight code generation
• AI Coding in Brownfield Projects: Applying research-first workflows to complex, legacy codebases
• Understanding AI Agents: Architecture and Patterns: Deep dive into agent architecture and production deployment

External Resources

• Research/Plan/Implement Deep Dive: Advanced Context Engineering for Coding Agents
• Subagents Documentation: Claude Code Subagents
• Related Technique: Ralph Wiggum as a Software Engineer - A complementary approach using infinite loops
• Team Workflow: How We Ship 6 PRs Per Engineer Per Day (12-Factor Agents)
• Industry Context: AI Engineer 2025 Talks - "Specs are the new code"

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This article synthesizes learnings from production teams using AI coding agents at scale, including detailed workflows from HumanLayer, techniques from the AI That Works community, and real-world results from projects like BAML. All techniques are battle-tested in complex, production codebases.