how we prevent ai agent's drift & code slop generation

#ai #genai #llm #mcp

At TraycerAI we reduced hallucinations, code slop generation and solved agent's drift. Finally you can ship complex features, end to end products with world's first AI product planner.

Traycer is built on the core philosophy of Spec driven development and is way ahead than anyone due to its approach.

Lets discuss, how we've achieved this

AI agents that write code have a dirty little secret, they drift, hallucinate, and generate confident garbage. After building AI agents that have executed large scale agentic workloads, here's what actually works to stop them.

The Core Problem

AI coding agents aren't stateless chatbots. They explore codebases, make decisions, loop through tool calls, and build context over time. Without proper guardrails, they drift from the original task, get stuck in infinite exploration cycles, or generate plausible-looking code that's fundamentally wrong.

The solution isn't one clever trick, it's a system of interlocking safeguards.

Circuit Breakers: Hard & Soft Thresholds

Every agentic loop needs circuit breakers. We implement a dual-threshold system:

At the warning threshold, the agent gets a nudge: "You've explored the codebase enough. Time to deliver your response." This redirects without forcing termination.

At the hard threshold, we force completion. The agent must use whatever context it has gathered and produce output, no more exploration allowed. Without this, agents will happily research forever, burning tokens while producing nothing.

Structured Output Contracts

Never trust raw LLM output. Every response passes through strict validation:
JSON/XML schema validation
Type checking against expected structures
Completeness verification (did we get all required fields?)

If output is malformed, we retry with explicit correction instructions. If it's still broken after retries, we fail gracefully with actionable error messages, not silent corruption.

Explicit Termination Tools

Here's a subtle but critical insight: don't let agents decide when they're "done" through natural language. Give them explicit termination tools they must invoke, complete_review, submit_plan, finish_verification.

This creates auditable completion points. We know exactly when and why an agent decided to terminate, and we can validate that the termination was legitimate.

Persistent State Management

Agent state is precious.
Connection tracking ensures state survives interruptions. LRU caching keeps frequently-accessed objects in memory for performance. If the agent crashes mid-task or the user closes their laptop, we can resume from the last checkpoint, not restart from scratch.

This isn't just about reliability. It's about building user trust. Nothing kills confidence faster than losing 10 minutes of agent work to a network hiccup.

AI Reviewing AI

This sounds counterintuitive, but it works: after code generation, a separate verification agent reviews the output with fresh eyes.

Does the change align with user intent?

Are there unintended side effects?

Did it miss edge cases the original agent overlooked?
Different model, different prompt, different perspective. The verification agent has no investment in defending the original output, its only job is finding problems.

Provider Redundancy

Single LLM provider equals single point of failure. Our routing layer maintains multiple providers per request type with automatic failover. If one provider hits rate limits or returns errors, we transparently switch to the next.

The agent doesn't even know which provider responded. This abstraction keeps the core logic clean while giving us resilience against the inevitable API hiccups.

Scope Boundaries

Vague scope produces vague output. We explicitly constrain agents: stay focused on the codebase, don't venture into production deployment, don't assume access to external systems you can't verify.

When agents know their boundaries, they make better decisions within them.

What Doesn't Work

A few anti-patterns we've learned to avoid:

"Just tell the agent to be careful" :- Prompting for carefulness doesn't prevent systematic drift
Single retry on failure :- Real-world errors need exponential backoff and provider failover
Implicit completion detection :- "The agent stopped talking, so it must be done" is a recipe for incomplete work
Trusting token counts for progress :- More tokens doesn't mean more useful work

The System, Not The Trick

Preventing AI agent drift isn't about one clever technique. It's about building a system where every layer catches what the others miss:

Thresholds prevent infinite loops. Validation catches malformed output. Persistent state enables recovery. Verification catches logical errors. Redundancy handles infrastructure failures.
Each safeguard is simple. Together, they create reliable agents.
Build the system. Don't hope for the best.

PS: These are patterns we’ve found effective across multiple agent systems.

What's your biggest challenge with AI agent reliability?