How AI Engineers Build Better Agents with Four Patterns
For AI engineers building production LLM agents · Based on Swanepoel's Best Agents Four-Pattern Framework
// TL;DR
AI engineers building production agents face a common arc: the prototype impresses, but users don't trust it in production. Swanepoel's Four-Pattern Framework gives you four structural patterns — Focus Modes, Transparent Execution, Personalization, and Reversibility — to close the gap between demo and deployment. Use it to audit your current agent architecture, identify the highest-leverage improvement, and iterate pattern by pattern instead of chasing vague quality issues across an unconstrained agent.
Why do users distrust my agent even when the outputs are correct?
The most common cause is missing Transparent Execution. When an agent delivers a result without showing how it got there — which tools it called, what sources it read, what assumptions it made — users have no basis for trust. This is especially true in high-stakes domains like finance, legal, or healthcare.
As an AI engineer, implement Transparent Execution by surfacing a live progress indicator, tool call logs with inputs and outputs, and an explicit list of assumptions and uncertainties. This shifts the user from blind delegation to active collaboration. Users who can see the process intervene earlier when something goes wrong, reducing wasted compute and rework cycles.
How do I stop my agent from being mediocre at everything?
The answer is Focus Modes. A general-purpose agent that handles any request tends to handle none of them exceptionally well. Identify the 2–5 distinct task types your agent performs — for a coding agent, this might be Plan, Code, Debug, and Review — and create a separate mode for each.
For each Focus Mode:
- Drop tools that aren't relevant to that mode
- Refine the system prompt to that mode's specific constraints
- Set explicit user-facing expectations about what the mode does and what inputs it needs
This constrained surface area makes evaluation tractable. Instead of trying to evaluate a do-anything agent holistically, you run targeted evals mode by mode and improve incrementally. This is where real quality gains happen.
How do I make my agent's outputs feel like the user wrote them?
Personalization is the pattern that separates production agents from demos. Build a Personalization layer with three mechanisms:
1. Playbooks — Encode the user's or organization's domain-specific methods. A consulting firm's research agent should structure analysis using their framework (e.g., MECE), not generic bullet points.
2. Memory — Persist learnings from past interactions. If a user always prefers concise outputs or rejects certain phrasings, the agent should remember and adapt.
3. Connected Systems — Integrate with the user's knowledge bases, tools, and data sources so the agent operates in their context, not in a vacuum.
The litmus test: would a colleague recognize the output as the user's own work?
How do I handle agent actions that could go wrong?
Engineer Reversibility at multiple granularity levels. For every action your agent takes that has a meaningful downside:
- Stage destructive actions as pending-confirmation before execution
- Log a rollback path for each action
- Integrate with platform-native change tracking (e.g., Git diffs, Word track-changes)
- Where possible, generate parallel outputs so the user picks the best and discards the rest
Reversibility is not about undoing failures after the fact — it's about making users bold enough to authorize high-value tasks in the first place. When the worst case is an undo, users say yes to experiments they'd otherwise reject.
What's the recommended implementation order?
Start by auditing your agent against all four patterns, scoring each as absent, partial, or present. Then implement in the order that addresses your biggest user pain point:
- Low trust → Transparent Execution first
- Generic outputs → Personalization first
- Fear of mistakes → Reversibility first
- Inconsistent quality → Focus Modes first
Each pattern can be evaluated and iterated independently, so you don't need a massive refactor. Ship one pattern, measure the impact, and move to the next.
Next step: Audit your current agent against all four patterns today. Score each one and identify your highest-leverage gap — then build a focused sprint around closing it.
// FREQUENTLY ASKED QUESTIONS
How do I evaluate each Focus Mode independently?
Run targeted evaluations scoped to each mode's specific task type. Create test cases that exercise only the tools and prompts active in that mode. Measure accuracy, relevance, and user acceptance rate per mode rather than across the whole agent. This isolates quality issues to specific modes and makes improvement measurable and incremental.
What's the minimum viable implementation of Transparent Execution?
At minimum, show users a step-by-step progress log of what the agent is doing, which tools it called, and what it found. Even a simple numbered list of completed and pending steps with tool names significantly increases trust. You can add richer transparency — assumption surfacing, uncertainty flags, source citations — iteratively after the baseline is in place.
How do I persist agent Memory across sessions?
Store user-specific learnings in a structured format — a vector store for semantic recall or a key-value store for explicit preferences. After each interaction, extract corrections, preferences, and context the agent should remember. On the next session, retrieve relevant memories and inject them into the system prompt or context window. Start simple with explicit preference logging before moving to automated memory extraction.