How Do Coding Agents Actually Work, and Why Do They Produce Inaccurate Outputs in Complex Codebases?

Preface: Demystify the Black Box

Most developers hold two deep-rooted misconceptions about AI coding assistants.

1. They treat AI tools as opaque black boxes: input requirements and expect flawless code as output.
2. They believe models with million-token context windows will perform better as long as sufficient background information is provided.

This approach works efficiently for simple, independent small tasks. However, in enterprise-grade projects featuring hundreds of thousands of lines of code, dozens of components, cross-module business logic, and mixed legacy & new code, the "black box mindset" quickly collapses.

You will observe the AI hallucinates false logic, ignores architectural conventions, and even generates code that is syntactically valid yet fatally flawed for business scenarios.

Previous community articles introduced Harness Engineering, summarizing Harness methodologies, pitfalls and practical experience from OpenAI, Anthropic, LangChain and more.

Nevertheless, all publicly available success cases apply to greenfield projects or brand-new Harness implementations. Applying these technologies to decade-old codebases lacking architectural constraints, inconsistent test suites and incomplete documentation poses far greater challenges.

Part 1: Core Principles

This section answers the fundamental questions: How do Coding Agents operate, and why do they degrade in complex repositories?

Chapter 1: Operating Mechanisms of Coding Agents

1.1 The Essence of LLMs: Autoregression + Attention Mechanism

To understand why Coding Agents make mistakes, you first need to grasp how their underlying "brain" functions.

Large Language Models only perform one core task: predict the next token. They possess no separate reasoning process independent of output — reasoning is generation, and generation is reasoning. The model generates one token at a time, appends it to the existing sequence, and continues predicting subsequent tokens. This process is known as autoregression.

The Attention Mechanism governs how the model "reads" preceding content at each step. You can visualize it as a dynamic spotlight: while generating new text, the model scans the entire context window and assigns varying weight ("attention") to every position. In theory, this spotlight can illuminate any section of text.

However, practical deployment reveals several critical inherent limitations:

Characteristic	Technical Definition	Impact on Programming Workloads
Sparse, Biased Attention	The model does not distribute focus evenly; it prioritizes text at the very start and end of context	Critical logic placed in the middle of context may be overlooked
O(N²) Computational Complexity	Doubling context length quadruples compute and VRAM consumption	Files cannot be infinitely appended to the context window
Diluted Attention Weights	Longer context reduces the attention share allocated to each individual token	Key information gets buried when dozens of files are mixed together

Worse yet, autoregression creates a hallucination snowball effect specifically for coding workloads.
Software development involves long workflows with rigid constraints. Once the model guesses an incorrect function name early (due to diluted attention obscuring the correct API), it will consistently build coherent yet invalid logic atop this error for every subsequent token until compilation fails.

Two critical takeaways from autoregression and Attention:

1. Model "reasoning" is probabilistic token continuation, not formal logical deduction.
2. Context quality vastly outweighs context quantity. Feeding 50 unrelated files into the window dilutes critical signal rather than improving model performance.

1.2 Reinforcement Learning (RL): From Text Generation to Action Execution

Pre-training only teaches models what source code looks like via massive GitHub datasets. It is analogous to someone who has read every cookbook yet never set foot in a kitchen — they know recipe formatting but cannot cook.

Reinforcement Learning (RL) equips Coding Agents with actionable capabilities. Instead of explicitly instructing the model how to complete tasks, RL only defines success criteria — an ideology perfectly aligned with software development: any implementation that passes tests is acceptable.

Modern coding models undergo RL training with the following workflow:

1. Execute programming tasks inside simulated environments
2. Use test pass rates and code quality scores as reward signals
3. Learn when to read additional files, when to perform edits, and how to extract actionable clues from error logs

Practical Prompt Guidance from RL Principles

Understanding RL reveals how to craft effective instructions for Agents:

• ❌ Poor instruction: Fix the login bug.
• ✅ High-quality instruction: Resolve unsaved login session state; all tests must pass after running npm test.

These two prompts appear similar to humans, but the latter provides verifiable success metrics that align directly with the reward signals the model learned during RL training. Clear, testable objectives drastically boost Agent performance.

1.3 The Agent Loop: Core Execution Cycle

LLMs can answer standalone questions, while Agents can complete multi-step tasks. The core distinction lies in the repeating loop:
Call LLM → Determine next action → Execute tool → Write tool output back to context → Re-invoke LLM

This repeating sequence is the Agent Loop.

Take a concrete example task: Fix the bug where user sessions fail to persist after login.

Iteration	Agent Behavior
Round 1	Call list_directory to inspect project root folders
Round 2	Identify the src/auth directory, invoke read_file to load login.js
Round 3	Detect calls to sessionManager.save(), read session/manager.js for implementation details
Round 4	Discover missing await for asynchronous logic inside save(), execute edit_file to patch the code
Round 5	Launch shell tool to run automated test suites
Round 6	Confirm all tests pass and generate a summary of the resolved issue

One vital detail: output from every single tool call is appended to the shared context window. This continuously inflates context size with file contents, shell outputs and error logs — the root cause of failures for lengthy tasks due to context pollution.

Chapter 2: Longer Context Does Not Equal Better Performance

After grasping LLMs, RL and Agent Loops, a natural question emerges: what defines the functional limits of Coding Agents?

Many developers assume the bottleneck is insufficient context window capacity. This statement is only half correct. The true challenge is not whether data can fit into context, but whether the model can reliably and accurately utilize the data once loaded.

We split Coding Agent context challenges into two distinct layers:

1. Capacity Limits: Hard technical caps on input size enforced by models and tooling
2. Utilization Limits: Even with data loaded, the model may fail to extract critical information

The second layer almost always determines real-world performance.

2.1 Physical Capacity vs Effective Context Window

Every LLM has a hard maximum context window size, dictated by underlying compute and memory costs. Traditional Attention algorithms scale quadratically with input length, slowing inference and increasing VRAM consumption as text grows longer.

"Extended context support" purely means the model can technically accept larger input payloads — it does not guarantee stable, accurate comprehension at maximum window size.

Long context windows solve storage constraints alone; they cannot resolve comprehension degradation.

2.2 Context Rot

Research published by Chroma titled Context Rot demonstrates that model performance consistently declines as input length increases. Degradation becomes far more severe when relevant information is semantically distant from the core task query.

Within Agent workflows, every grep result and loaded file that proves irrelevant acts as attention noise, degrading the model’s accuracy in subsequent iterations.

In practical development scenarios, the core pain point is not the model lacking access to information, but its inability to reliably isolate critical signals amid noise.

This is the most widely misunderstood property of long context windows:
Developers often dump requirement docs, architecture diagrams, API specs and historical discussions into context simultaneously under the assumption "more information = better results." This frequently backfires:

• Extended text dilutes attention weights
• Irrelevant content buries critical clues
• When questions and relevant code are separated by large blocks of text, the model prioritizes superficially matching irrelevant snippets

The core challenge introduced by long context is not data storage, but noise mitigation.

2.3 Lost in the Middle: U-Shaped Attention Distribution

The "Lost in the Middle" phenomenon predates Context Rot research. A 2024 Stanford study published in TACL proved LLM attention follows a U-shaped curve: the highest focus is allocated to text at the absolute start and end of context, while content in the middle receives minimal weight.

In multi-document testing with 20 input files, accuracy dropped over 30% when the document containing the correct answer was placed in positions 5–15 (the middle zone), versus placement at the first or last position.

For Coding Agents, this creates tangible failures: if the Agent loads 8 source files while the critical business logic resides in the 4th file, the model will overlook the key implementation due to the attention blind spot in the middle of context.

Key Conclusion: Context management is a rigorous engineering discipline, not just loading all available text. Precise, concise context outperforms bloated, noisy context. As Anthropic outlined in its Context Engineering blog: context is a finite resource with diminishing marginal returns.

2.4 Three Fundamental Limitations of Current Coding Agents

1. Irreversible Bias Accumulation
   Once the model misinterprets logic in an early iteration, all subsequent reasoning and code generation builds upon this flawed premise. In coding tasks, this manifests as locally consistent yet globally incorrect code: individual lines, function calls and naming standards appear valid, yet core assumptions about function responsibilities or API semantics are fundamentally wrong. Many AI-generated bugs are logically sound in isolation but broken end-to-end.

2. Limited Multi-Constraint Handling
   Real-world development enforces simultaneous rules: syntax validity, semantic logic, directory structure, legacy compatibility, test coverage, performance budgets, security boundaries and team style guides. Agents struggle to maintain compliance with all constraints over long task chains. Fixing one bug often breaks compatibility or violates coding standards.

3. Natural Serial Exploration Bias
   Senior human developers maintain multiple parallel hypotheses and eliminate them iteratively when debugging. Most Agents default to serial exploration: they pursue one line of investigation fully before testing alternatives, frequently wasting resources on dead-end paths.

Now that we understand why Agents degrade in complex codebases, we examine how tooling compensates for these inherent flaws.

Part 2: Tooling Analysis

This section avoids head-to-head comparisons between Coding Agent products. Instead, it breaks down the core mechanisms different tools use to offset unstable comprehension of large repositories.

Chapter 3: Four Paradigms for Code-Aware AI Tooling

3.1 Evaluate Context Systems, Not Just Underlying Models

Comparing model parameter counts cannot explain real-world performance gaps. In production projects, the decisive factor is not the model’s raw code generation ability, but how the tool interfaces with the repository, ingests project constraints, forms closed action loops, integrates external extensions, and preserves reusable team knowledge.

We analyze tools across five core evaluation dimensions:

1. Context Ingestion: Does the tool rely primarily on semantic retrieval/file scanning, or explicit knowledge files (specs, rules, SKILL.md, AGENTS.md)?
2. Persistent Constraints: Are project standards stored as one-off temporary prompts per session, or version-controlled repository assets reusable long-term?
3. Action Loops: Does the tool only output static suggestions, or autonomously read/edit files, run shell commands and iterate based on feedback?
4. External Extensibility: Does it support MCP, hooks, subagents, plugins and skill packages to inject custom workflows?
5. Team Governance: Are optimization practices individual developer tricks, or standardized project/team engineering assets?

All modern tools including Cursor, Claude Code, Codex CLI and Kiro implement full Agent Loop functionality (file read/write, command execution, iterative fixes) alongside MCP, subagent and skill extensions. Standardized knowledge files such as SKILL.md and AGENTS.md are cross-compatible across platforms.

Our comparison focuses on each tool’s unique prioritized optimization direction, rather than feature checklists.

3.2 Cursor: Native IDE Agent with Optimized Retrieval & Context Recall

Cursor is one of the most mature AI-first IDEs built atop VS Code, featuring full multi-step task planning, multi-file editing, terminal automation and iterative refinement until tests pass.

Version 2.4 (Jan 2026) introduced subagents (isolated parallel child agents with independent context windows) and SKILL.md skill packages. Version 2.5 (Feb 2026) expanded the plugin ecosystem, packaging skills, subagents, MCP servers and hooks into installable marketplace modules. Additional offerings include Cloud Agents (isolated cloud VMs triggered via webhooks/Slack/GitHub) and scheduled Automations workflows.

Cursor’s defining competitive advantage lies in repository indexing and context retrieval deeply integrated within the IDE workflow.

Most AI coding errors stem from misidentifying relevant source files rather than flawed generation logic: the Agent may load semantically similar modules with unrelated business logic, or overlook critical rule/config files governing system behavior. Cursor mitigates this via heavy investment in repository indexing infrastructure:

• Full semantic vector indexing of the entire codebase runs in the background on first project open, building a structural repository map before any Agent session starts.
• Four-tier rule system: project-level .cursor/rules, user-level, team-level and session-level AGENTS.md to consistently inject constraints into every interaction.
• Dynamic skill loading prevents dumping all project knowledge into the context window simultaneously.

Vector RAG Workflow

Scan full repository files
↓
Split code by semantic blocks (functions/classes, not arbitrary line breaks)
↓
Generate embedding vectors for each logical code block
↓ Store embeddings in Turbopuffer vector database
↓ On user query: vectorize requirements, perform nearest-neighbor semantic search
↓ Inject matching code blocks + user prompt into LLM context

Even functions with no "auth" keyword in their names will be retrieved if their embedding semantically matches user authentication requirements. Code is split along syntactic boundaries rather than arbitrary line cuts for superior retrieval quality.
Limitations: Embedding vectors are stored on Cursor’s cloud servers (raw source remains local); initial indexing consumes high CPU/RAM for repositories exceeding 100,000 files.

Differentiator: Pioneered deep repository semantic retrieval natively embedded within the end-to-end IDE workflow.

3.3 Claude Code: Orchestratable, Governable Agent Exploration Pipelines

Where Cursor prioritizes in-IDE retrieval UX, Claude Code’s core focus is building composable, manageable exploration loops. It recognizes full repository comprehension rarely occurs in a single pass — understanding evolves through cycles of exploration, execution, feedback and revision that require structured orchestration, task division and centralized governance.

Live Grepping Workflow

Claude Code does not rely on pre-built RAG indexes, per engineering statements shared on Hacker News. Instead, it equips the Agent with raw filesystem tools to actively discover code on demand:

User query: Locate authentication logic
↓ Agent executes grep -r "auth" . → 87 matching results
↓ Agent executes grep -r "guard" . → 23 matching results
↓ Glob pattern search for **/*.interceptor.ts
↓ Read auth.interceptor.ts and trace call chains recursively
↓ Final identification of core authentication implementation

This multi-round tool workflow consumes 3–4x more tokens than indexed retrieval. The tradeoff is offset by a 1M-token ultra-large context window capable of loading entire modules in one payload, bypassing semantic retrieval weaknesses. Teams can also maintain a root CLAUDE.md manual repository map to skip redundant exploration steps.

Differentiator: Structured orchestration of multi-step discovery workflows for unindexed, complex codebases.

3.4 Codex: Low-Friction Execution & Validation Loops

Codex’s unique optimization streamlines code modification, execution and validation into tight, low-overhead cycles. Its core design assumption: many development tasks do not require full repository comprehension upfront. Fast local feedback accelerates convergence for well-scoped work such as lint fixes, test generation, bulk refactoring and isolated bug patches.

Codex excels at batch test generation, parallel independent task execution and background asynchronous workflows developers can resume later after meetings. Users describe it as a reliable long-running task executor rather than an interactive paired coding assistant.

Differentiator: Minimize convergence latency via lightweight local test/execution pipelines.

3.5 Kiro: Spec-First Structured Task & Long-Term Knowledge Management

Kiro is an AWS VS Code-based AI IDE with a distinct design philosophy focused on task formalization before writing any code. Many hallucinations originate from ambiguous requirements and missing persistent project context, which Kiro resolves through structured pre-coding documentation.

Core Three-Tier Mechanism

1. Spec-Driven Development After users input natural language requirements, Kiro auto-generates version-controlled Markdown specs stored under .kiro/specs:
2. requirements.md: Formal user stories + acceptance criteria
3. design.md: Database schemas, API endpoints, TypeScript interfaces, Mermaid data flow diagrams
4. tasks.md: Ordered implementation subtasks with dependencies

Example e-commerce review feature workflow:

Step 1: Generate Requirements Document
Define user submission windows, star ratings, merchant reply functionality and sorting rules.

Step 2: Generate Design Document
Database Review table schema, REST API routes, frontend component interfaces.

Step 3: Generate Task Breakdown
Ordered actionable subtasks linked to corresponding requirements.

Step 4: Execute Individual Tasks
Launch coding workstreams for each subtask with built unit test coverage requirements.

All spec files are tracked via Git for diff tracking, code review and historical audit trails. Developers report cutting two weeks of trial-and-error work via two hours of spec drafting, with automated high test coverage as a secondary benefit.

1. Steering Persistent Knowledge Files Running Kiro: Setup Steering auto-generates version-controlled project knowledge docs under .kiro/steering:
2. product.md: Product positioning and target user base
3. structure.md: Repository directory architecture rules
4. tech.md: Standard tech stack and tooling constraints Custom architecture/security rules can be manually added, and the tool automatically references these standards for every Agent session. Fully compatible with the open AGENTS.md standard.

Best For: Large collaborative teams, regulated audit-heavy industries, AWS cloud-native projects.
Less Ideal: Tiny one-line hotfixes with excessive spec setup overhead.

1. Interoperable SKILL.md Skill Packages Identical skill file format as Cursor and Claude Code for reusable domain workflow modules.

Differentiator: Eliminate pre-task ambiguity by formalizing requirements, architecture and team standards into persistent, reusable repository assets.

3.6 Summary: Tool Feature Convergence, Knowledge Engineering Is The Real Divide

All four tools share overlapping core functionality, with differentiation rooted in their prioritized context compensation strategy:

Platform	Unique Differentiator
Cursor	Indexed semantic retrieval + explicit @ reference context injection, parallel cloud subagent processing
Claude Code	Dynamic on-demand exploration, shared multi-agent context merging with explosion guardrails
Codex	Short-lived session pipelines, sequential agent chaining centered on shell execution feedback
Kiro	Pre-structured spec requirements + persistent version-controlled knowledge base

As feature parity and cross-compatible knowledge file standards expand, the decisive competitive advantage shifts to internal project knowledge infrastructure:

Raw Code Repository
↓ Reverse-engineer standardized rule docs (.cursorrules / CLAUDE.md / Kiro Steering)
↓ Inject structured knowledge into AI Agents
↓ Generate compliant new source code
↓ Synchronize updated standards back to repository (closed loop)

Tooling and LLMs will continuously update, but structured project knowledge assets compound team value over time — this is the core competitive edge for AI-native engineering workflows.

Last updated: March 2026. All analysis based on public documentation and hands-on testing; platform functionality subject to frequent official updates.

Appendix: Reference Materials

Research & Context Theory

• Chroma, Context Rot: How Increasing Input Tokens Impacts LLM Performance (July 2025) — https://research.trychroma.com/context-rot

Cursor Official Resources

• Cursor Agent Overview Docs: https://cursor.com/docs/agent/overview
• Cursor Agent Best Practices Blog: https://cursor.com/blog/agent-best-practices
• Cursor 2.4 Changelog (Subagents & Skills): https://cursor.com/changelog/2-4
• Cursor 2.5 Changelog (Plugins): https://cursor.com/changelog/2-5
• Cloud Agents Blog: https://cursor.com/blog/cloud-agents
• Self-Hosted Cloud Agents Blog: https://cursor.com/blog/self-hosted-cloud-agents

Claude Code Official Resources

• Subagent Docs: https://code.claude.com/docs/en/sub-agents
• Agent Teams Docs: https://code.claude.com/docs/en/agent-teams
• Hooks Docs: https://code.claude.com/docs/en/hooks

Codex CLI Official Resources

• Codex CLI Overview: https://developers.openai.com/codex/cli
• Codex CLI Feature Guide: https://developers.openai.com/codex/features
• Codex MCP Docs: https://developers.openai.com/codex/mcp
• Agents SDK Integration Guide: https://developers.openai.com/codex/guides/agents-sdk

Kiro Official Resources

• Kiro Official Site: https://kiro.dev
• Steering Docs: https://kiro.dev/docs/steering/
• Agent Skill Docs: https://kiro.dev/docs/skills/
• Custom Subagent & Skill Blog: https://kiro.dev/blog/custom-subagents-skills-and-enterprise-controls/

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