Building Agents that Actually Work

The promise of agentic AI systems is intoxicating: autonomous agents that can reason, plan, and execute complex tasks with minimal human intervention. Yet most agents fail spectacularly in production. They hallucinate, get stuck in loops, or simply don't deliver the value promised in demos.

After building and deploying agentic systems at scale, I've learned that the gap between demo and production isn't about model capabilities—it's about the engineering discipline required to build reliable, evaluable systems. This post outlines the modern challenges, industry patterns, and the evaluation-first approach that separates working agents from vaporware.

The Modern Challenge: From Prompt Engineering to Context Engineering

The industry has evolved beyond simple prompt engineering. Today's challenge is context engineering—the systematic design of how information flows through your agent, how it makes decisions, and how it maintains coherence across complex, multi-step tasks.

Traditional prompt engineering focused on crafting the perfect input. Context engineering focuses on designing the entire information architecture that enables an agent to maintain state, reason effectively, and produce consistent outputs across diverse scenarios.

The Three Pillars of Context Engineering

1. Information Architecture: How you structure and prioritize context
2. Decision Boundaries: Clear rules for when and how the agent acts
3. State Management: Maintaining coherence across interactions

Industry Patterns: What Actually Works

After analyzing dozens of production agentic systems, I've identified several patterns that consistently lead to success:

1. The Evaluation-First Development Cycle

Most teams build agents by iterating on prompts and hoping for the best. Successful teams build evaluation frameworks first, then optimize against measurable criteria.

If you can't measure it, you can't improve it. This is especially true for agentic systems where the failure modes are complex and often subtle.

The evaluation-first approach means:

• Define success metrics before writing a single prompt
• Build test suites that cover edge cases and failure modes
• Automate evaluation so you can iterate quickly
• Measure in production to catch real-world issues

2. The Context Window Paradox

Larger context windows don't automatically lead to better performance. In fact, they often lead to worse results due to:

• Information overload: The agent gets distracted by irrelevant details
• Decision paralysis: Too many options lead to inaction
• Cost explosion: Larger contexts are exponentially more expensive

The solution is context distillation—systematically filtering and prioritizing information based on relevance to the current task.

3. The Memory Hierarchy Pattern

Effective agents use a three-tier memory system:

1. Working Memory: Current task context (limited, high-priority)
2. Episodic Memory: Recent interactions and outcomes
3. Semantic Memory: Long-term knowledge and patterns

Each tier has different access patterns, update frequencies, and capacity constraints. Designing this hierarchy is crucial for both performance and cost management.

Evaluations: The Foundation of Quality Agents

Evaluations are not just a nice-to-have—they're the foundation of building reliable agentic systems. Here's how to implement them effectively:

1. Multi-Dimensional Evaluation Framework

Don't just measure accuracy. Build evaluations that assess:

• Task Completion: Did the agent accomplish the intended goal?
• Reasoning Quality: Was the decision-making process sound?
• Efficiency: Did it use resources (tokens, API calls) effectively?
• Safety: Did it avoid harmful or inappropriate outputs?
• User Satisfaction: Did the user get value from the interaction?

2. Automated Evaluation Pipelines

Manual evaluation doesn't scale. Build automated pipelines that:

• Generate test cases from real user interactions
• Run regression tests on every deployment
• Measure drift in agent behavior over time
• Alert on anomalies before they reach users

3. Human-in-the-Loop Validation

Automated evaluations catch obvious issues, but human judgment is still essential for:

• Edge case identification: Humans spot patterns machines miss
• Quality assessment: Subjective aspects like tone and style
• Bias detection: Identifying problematic patterns
• User experience evaluation: How the interaction feels

Production Axioms: Rules That Work

These axioms have proven themselves across multiple production deployments:

Axiom 1: Fail Fast, Fail Explicitly

Agents should fail quickly and clearly when they can't complete a task, rather than producing plausible but incorrect outputs. This requires:

• Confidence scoring on all outputs
• Clear error messages that explain what went wrong
• Graceful degradation to simpler approaches

Axiom 2: Context is a Scarce Resource

Treat context window space like you treat database connections or API rate limits. Every token should earn its place through relevance and value.

Axiom 3: Agents Need Feedback Loops

Static agents become stale quickly. Build systems that learn from:

• User feedback (explicit ratings, corrections)
• Outcome tracking (did the user's goal get accomplished?)
• A/B testing of different approaches
• Performance monitoring in production

Implementation Strategy

Here's the development process that consistently produces working agents:

Phase 1: Define and Measure

1. Define clear success criteria for your agent
2. Build evaluation datasets that represent real usage
3. Establish baseline performance metrics
4. Set up automated evaluation pipelines

Phase 2: Design Context Architecture

1. Map the information flow through your system
2. Design the memory hierarchy
3. Implement context distillation strategies
4. Set up monitoring for context efficiency

Phase 3: Iterate and Optimize

1. Run experiments against your evaluation framework
2. Measure impact on all dimensions (accuracy, cost, speed)
3. Implement feedback loops for continuous improvement
4. Monitor for drift and regressions

Common Pitfalls to Avoid

These patterns consistently lead to failure:

• Over-engineering: Complex systems are harder to debug and maintain
• Ignoring costs: Context windows and API calls add up quickly
• No evaluation framework: You can't improve what you can't measure
• Static deployment: Agents need to evolve with usage patterns
• Ignoring user feedback: The best evaluation comes from real users

Conclusion

Building agentic systems that actually work requires a fundamental shift from prompt engineering to context engineering, with evaluations as the foundation of quality. The teams that succeed are those that treat agent development as a rigorous engineering discipline rather than an art form.

The key insight is that agentic systems are not just about the model—they're about the entire information architecture, decision-making framework, and evaluation ecosystem that surrounds it. Get these right, and you'll build agents that deliver real value in production.

The future belongs to teams that can systematically design, evaluate, and improve agentic systems. The tools and patterns are there—it's time to use them.