AI Agents Are Powerful But Context-Dependent
AI coding agents can implement complete features rapidly. But software development requires more than generating code.
Context Loss
Agents can lose important architectural, business and technical context during development sessions, leading to decisions that ignore project constraints.
Uncontrolled Changes
Without explicit boundaries, an agent may modify critical infrastructure, configuration files, or architectural components it should not touch.
Inconsistent Decisions
Different prompts and sessions produce different implementations of the same feature or pattern, breaking architectural consistency.
Architectural Drift
Individually reasonable AI-generated changes gradually move a system away from its intended design without anyone noticing until it's too late.
Governance Gap
Traditional development doesn't have mechanisms to govern AI agent behavior. There are no explicit rules, responsibilities or controlled context.
No Session Continuity
Context established in one session can be lost when the agent is invoked again, requiring humans to re-explain the same constraints repeatedly.
The Deep Problem
The fundamental issue is not that agents generate incorrect code.
Agents operate on context → Context can be incomplete, inconsistent, stale, ambiguous, or uncontrolled → Unpredictable behavior
Why Traditional Approaches Fall Short
Reviewing agent output after it's generated is reactive and doesn't address the root cause.
Code Review Alone
Reviewing the output doesn't help the agent make better decisions next time. The context governance problem persists.
Better Prompts
More detailed prompts help temporarily, but they're not persistent, reviewable, or governance mechanisms.
Trust in the Agent
Trusting the agent to "follow the architecture" doesn't work when the architecture isn't explicitly governed and protected.
Manual Oversight
Humans manually reviewing every change doesn't scale and misses the real issue: lack of governed context.
The Solution
Make context explicit, governed and protected.
GTT-Method addresses this by treating context as an engineering asset that can be structured, governed, protected and validated.
Instead of trying to make agents "smarter", GTT-Method makes the development environment more reliable by establishing explicit rules, protecting critical context, and detecting drift.