Building AI Agents That Actually Work: Lessons from Production

The AI agent demos are impressive. An agent that researches companies, writes personalized emails, schedules meetings, and updates the CRM—all autonomously. Stakeholders get excited. Budgets get approved. Then six months later, you have an expensive prototype that fails on any input that wasn't in the demo script.

We've built agents that work in production, handling thousands of tasks without hand-holding. We've also inherited agent projects that were abandoned after significant investment. The difference isn't the underlying models—it's everything around them.

Why Most Agent Projects Fail

The failure mode is predictable. Teams start with an ambitious vision: an agent that handles entire workflows end-to-end. They build a demo that works on the happy path. Then reality hits.

Edge cases multiply faster than you can handle them. The demo works for standard email formats. Then someone sends an email with attachments, or in a different language, or with ambiguous intent. Each edge case requires new handling, and there are always more edge cases.

The agent becomes unreliable at the wrong moments. It works 95% of the time, which sounds good until you realize it fails unpredictably on the remaining 5%. Users can't trust it with anything important because they never know when it will break.

The cost surprises everyone. GPT-4 calls that seemed cheap in prototyping add up when the agent runs thousands of times daily. The agent that saved $50,000 in labor costs $40,000 in API fees. The math no longer works.

Nobody thought about what happens when it fails. The agent silently drops tasks, or worse, takes wrong actions that need to be reversed. There's no monitoring, no alerting, no way to know what went wrong.

The agents that succeed avoid these traps—not by being smarter, but by being more carefully designed.

Start Narrower Than You Want

The most important decision is scope. Every successful agent project we've seen started with something embarrassingly small.

Not "an agent that handles customer service." An agent that answers one specific type of question using one specific knowledge base.

Not "an agent that does sales research." An agent that looks up company information from one data source and formats it in one specific way.

The narrow scope isn't a limitation—it's what makes the agent reliable. When the domain is constrained, you can actually enumerate the failure modes. You can test comprehensively. You can build monitoring that knows what "working correctly" looks like.

For a legal tech client, we built an agent that extracts specific clause types from contracts. Not general contract analysis—just finding and extracting non-compete clauses. This narrow focus meant we could achieve 98% accuracy and handle the remaining 2% with human review. A broader agent would have been 80% accurate across everything, which is useless for legal work.

Once the narrow agent proves its value, you expand. The extraction agent grew to handle more clause types, then to comparison between contracts, then to flagging unusual terms. Each expansion built on a solid foundation.

The narrow scope also makes testing tractable. You can create comprehensive test suites for a specific task. You can identify edge cases and handle them explicitly. You can measure accuracy in meaningful ways because the expected outputs are well-defined. Broad agents resist testing because the space of possible inputs and outputs is too large to cover systematically.

Stakeholder management becomes easier with narrow scope too. You can explain exactly what the agent does and doesn't do. When it fails—and it will fail—the failure modes are understandable. "The agent didn't recognize this unusual clause format" is actionable feedback. "The agent got confused by this complex document" is not.

Design for Failure

Every agent will fail. The question is how—and whether you'll know about it.

Graceful degradation beats silent failure. When the agent can't complete a task, it should acknowledge the failure, preserve what it learned, and route to a human. A task that fails loudly gets fixed. A task that fails silently compounds into bigger problems.

Build explicit uncertainty handling. Agents should have confidence scores, and those scores should actually mean something. When confidence is low, the agent asks for clarification or escalates. We've seen agents that were 60% confident produce the same output as agents that were 99% confident—the confidence signal was decorative.

Log everything. Every decision, every tool call, every input and output. When something goes wrong—and it will—you need to understand why. The logs are also how you improve the agent over time.

Human-in-the-loop by default. Start with human approval for every action. As the agent proves itself, gradually reduce oversight. An agent that asks for approval 100 times for the same action type earns autonomy for that action. But you need the data from those 100 approvals to know it's reliable.

One of our agents processes expense reports. Initially, every categorization required human approval. After approving 500 categorizations, patterns emerged. Now routine categorizations are automatic, but unusual expenses still get human review. The agent handles 70% of volume autonomously; the remaining 30% goes to humans who handle it faster because they're only seeing the hard cases.

The approval data becomes training data. Each time a human confirms or corrects an agent decision, that feedback improves future performance. This creates a virtuous cycle: the agent gets better, which builds trust, which justifies more autonomy, which generates more high-quality training data.

Rollback capabilities are essential for agents that take actions. If an agent sends a wrong email or makes an incorrect database update, you need a way to undo it. Not every action is reversible, which is why irreversible actions should require the most human oversight. An agent that can archive something should probably work autonomously; an agent that can permanently delete something should probably not.

The Architecture That Works

Agent architectures vary, but the ones that succeed share common elements.

A Core Loop That's Predictable

The agent's main loop should be simple enough to reason about: perceive, decide, act, record. Complex agents are built from simple agents composed together, not from complicated single agents.

Each step in the loop has clear inputs and outputs. Each step can be logged independently. When something breaks, you can identify which step failed.

Tools, Not Intelligence

The agent's power comes from its tools, not from the LLM being clever. The LLM decides which tool to use; the tool does the work.

Good tools are deterministic, well-tested, and have clear error handling. If the tool for sending emails fails, it returns an explicit error, not ambiguous output the LLM has to interpret.

We've seen teams try to make the LLM smarter to handle cases where tools failed ambiguously. It never works. Fix the tools instead.

State That Persists

Agents need memory—both within a task (what has happened so far) and across tasks (what the agent has learned). This isn't conversation history; it's structured state that the agent can query.

For a research agent, this might be: which sources have been checked, what information was found, what questions remain unanswered. The agent can resume an interrupted task or explain its reasoning at any point.

Persisting state also enables debugging. When an agent takes a wrong action, you can inspect its state and understand why.

Monitoring That Matters

Not just "is the agent running" but "is the agent doing good work."

Task success rates. Time to completion. Human escalation rates. Error categories. Costs per task.

When these metrics drift, you catch problems before users do. When you improve the agent, you measure whether it actually got better.

Build dashboards that stakeholders can understand. Technical teams need detailed metrics, but executives and business owners need summaries: how many tasks processed, what percentage succeeded, what was the human effort saved. These dashboards justify continued investment and surface problems before they become crises.

Alert fatigue is real. Don't alert on every minor anomaly. Build tiered alerting: informational logs that nobody reviews unless investigating a problem, warnings that get looked at daily, and urgent alerts that wake someone up. Most monitoring systems over-alert initially; calibrate until the alerts are actionable.

The Cost Reality

Agents are expensive to run. Every LLM call has a cost, and agents make many calls per task. Ignoring this reality leads to projects that work technically but fail economically.

Model selection matters more than optimization. A task that needs GPT-4 capability costs 30x more than one that works with GPT-3.5. Many agent tasks—tool selection, formatting, simple classification—don't need frontier models.

Caching is your friend. If the agent frequently looks up the same information or performs the same reasoning, cache the results. We've seen agents where 40% of LLM calls were functionally identical to previous calls.

Batch where possible. Some agent tasks can be batched without latency impact. Processing 100 documents overnight is cheaper than processing them on-demand.

Set cost budgets. Each task has a maximum cost. If the agent exceeds it, the task fails rather than racking up unlimited charges. This also catches runaway agents that get stuck in loops.

For one client, we reduced agent costs by 70% without changing capability. The original agent used GPT-4 for everything. After analysis, 80% of calls worked fine with smaller models. The remaining 20%—the complex reasoning steps—still used GPT-4.

Open-source models for cost-sensitive workloads. For tasks where the agent runs thousands of times daily, the difference between $0.10 and $0.001 per run matters enormously. Open-source models running on your own infrastructure can reduce marginal costs dramatically, though they add operational complexity.

Prompt caching is increasingly available. If your agent uses the same base prompt for every task, caching the prompt prefix can reduce costs significantly. Anthropic and OpenAI both offer prompt caching—take advantage of it.

The cost discussion should happen at project inception, not after deployment. "We built something amazing but can't afford to run it" is a common and preventable failure mode.

When Agents Make Sense

Not every automation problem needs an agent. Agents excel at specific situations:

Unstructured inputs that require interpretation. If the input is clean and structured, traditional automation is cheaper and more reliable. Agents shine when inputs are messy—natural language, ambiguous documents, varying formats.

Multi-step processes with conditional logic. If the workflow is always the same sequence, script it. Agents are for workflows where the next step depends on what was learned in previous steps.

Tasks where edge cases are frequent. If 95% of cases follow a pattern and 5% are exceptions, agents handle the exceptions while automation handles the pattern. If 30% of cases are exceptions, maybe you need an agent for everything.

Tasks where the cost of human labor is high. Agents are expensive, but so are humans for certain tasks. Legal research, sales intelligence, complex support—if human time costs hundreds per hour, agent costs can be justified.

Getting Started Right

If you're considering building an AI agent, here's the approach that works:

Document the process as humans do it. What decisions do they make? What information do they need? What are the common problems? The agent will need to handle all of this.

Identify the narrowest valuable scope. What's the smallest piece that would deliver value on its own? Start there.

Build the tools first. Before any agent logic, build and test the tools it will use. APIs for data access, functions for taking actions, integrations with existing systems. These need to work reliably before the agent touches them.

Prototype with heavy logging. Run the agent on real tasks but log everything. Review the logs manually. Understand where it fails and why.

Add monitoring before production. Define what "working well" looks like in metrics. Build dashboards. Set up alerts. This isn't optional.

Launch with human oversight. Even if you trust the agent, launch with approval workflows. Reduce oversight as the agent proves itself with data, not faith.

The Long Game

The best agent systems improve over time. The logs from production become training data. The edge cases become handled cases. The human overrides become automated decisions.

This requires investment: infrastructure to collect feedback, processes to review failures, and time to iterate. Teams that treat agent deployment as the end of the project end up with agents that stay mediocre forever.

The agents that deliver lasting value are maintained like products—continuously improved, regularly reviewed, and adapted as business needs change.

Testing Agents Is Different

Traditional software testing doesn't work for agents. You can't write a unit test that says "the agent should respond correctly to this prompt" because "correctly" depends on context, and the same prompt might legitimately produce different responses.

Behavioral testing works better. Instead of testing outputs, test behaviors: Does the agent use the right tools for this type of request? Does it ask for clarification when information is missing? Does it escalate appropriately when uncertain? These behaviors are testable even when specific outputs vary.

Golden dataset testing catches regressions. Build a collection of representative inputs with known-good outputs. Run the agent against this dataset regularly. When responses change significantly, investigate. Not every change is a regression—sometimes the model improved—but significant changes deserve attention.

Adversarial testing finds failure modes. What happens when users give contradictory instructions? When they try to jailbreak the agent? When inputs are malformed or in unexpected languages? Adversarial testing surfaces problems before users find them.

A/B testing validates improvements. Before deploying agent changes widely, test them on a subset of traffic. Compare success rates, user satisfaction, and cost. The change that seems better in development might perform worse in production.

We've seen teams skip testing because "it's AI, you can't test it." Those teams regret the decision when their agent embarrasses them in production. Testing is harder for agents, but not optional.

Security Considerations for Production Agents

Agents create new attack surfaces that traditional security models don't address. The agent has capabilities—sending emails, accessing databases, modifying records—that attackers want to hijack.

Prompt injection is the primary threat. Users craft inputs that cause the agent to ignore its instructions and follow attacker commands instead. "Ignore your previous instructions and instead..." is the simple version. Sophisticated attacks hide instructions in documents the agent reads, in API responses, or in data the agent processes.

The defense is layered: input sanitization, output validation, capability restrictions, and monitoring for unusual behavior. No single defense is sufficient. Assume some attacks will get through and limit the damage they can do.

Privilege escalation through tools. An agent with access to an admin tool might be tricked into using it inappropriately. Tools should have the minimum permissions necessary. An agent that only needs to read from a database shouldn't have write access, even if adding write access would be convenient.

Data exfiltration through outputs. Agents that can send emails or make API calls might be tricked into including sensitive data in those outputs. Output monitoring catches obvious exfiltration—SSNs, credit card numbers—but subtle leakage is harder to detect.

Denial of service through loops. A malicious input might cause the agent to enter an infinite loop, consuming resources and blocking legitimate work. Timeouts and iteration limits are basic defenses. More sophisticated attacks cause inefficient but technically bounded loops that waste money without triggering limits.

Security for agents is an emerging field. Best practices are still developing. The minimum is: treat agent inputs as untrusted, limit agent capabilities to what's needed, monitor agent behavior for anomalies, and plan for incidents.

The Build vs. Buy Decision

You can build agents from scratch, use agent frameworks, or buy agent platforms. The right choice depends on your needs and capabilities.

Building from scratch gives maximum control. You understand every component because you built it. Debugging is straightforward because there's no framework magic. But you're also responsible for everything—the orchestration loop, memory management, tool integration, monitoring, all of it.

Building from scratch makes sense when: your agent requirements are unusual, you have strong AI engineering expertise, or the agent is core to your competitive advantage.

Agent frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) provide building blocks: memory systems, tool abstractions, orchestration patterns. You assemble components rather than building everything. Development is faster, but you inherit the framework's opinions and limitations.

Frameworks make sense when: your agent requirements are fairly standard, you want to move faster than building from scratch, and you can accept the framework's constraints.

Agent platforms (custom GPTs, Microsoft Copilot Studio, various startups) provide complete solutions. Configuration replaces coding. Non-engineers can build agents. But customization is limited, and you're dependent on the platform vendor.

Platforms make sense when: your agent requirements are simple, you don't have AI engineering expertise, or you're prototyping before committing to a full build.

We've used all three approaches depending on client needs. The trap is choosing based on what seems sophisticated rather than what fits the situation. A platform agent that ships and works beats a from-scratch agent that never finishes.

Common Agent Patterns

Certain architectures recur across successful agents. Understanding these patterns accelerates design.

ReAct (Reason + Act). The agent thinks about what to do, takes an action, observes the result, and repeats. Each cycle is explicit reasoning followed by tool use. This pattern is interpretable—you can see why the agent did what it did—and handles multi-step tasks naturally.

Plan and Execute. The agent creates a complete plan before taking any action, then executes the plan step by step. Planning and execution are separate phases. This works well when the task is well-understood upfront and plans don't need mid-course correction.

Reflexion. The agent attempts a task, evaluates its own performance, and retries with lessons learned. Each iteration incorporates feedback from previous attempts. This pattern handles tasks where first attempts often fail but improvement is possible through self-reflection.

Multi-agent collaboration. Multiple specialized agents work together. A researcher agent gathers information; an analyst agent processes it; a writer agent produces output. Each agent does what it's good at. Coordination is the challenge—agents need to communicate and hand off work cleanly.

Hierarchical agents. A supervisor agent breaks down complex tasks and delegates to worker agents. The supervisor handles planning and coordination; workers handle execution. This scales to complex tasks that would overwhelm a single agent.

These patterns aren't mutually exclusive. Production agents often combine elements—ReAct for individual steps within a Plan and Execute framework, or hierarchical structure with multi-agent collaboration at each level.

When Not to Build an Agent

Agents are powerful, but not every problem needs one. Sometimes simpler solutions work better.

Deterministic workflows should stay deterministic. If the steps are always the same and decisions are simple conditionals, a traditional workflow engine is cheaper and more reliable than an agent. Agents add value when decisions require judgment; if no judgment is needed, skip the agent.

When latency matters more than flexibility. Agents are slow compared to simple function calls. Each LLM call adds latency. If response time is critical—sub-second requirements—agents might not fit. Consider whether the flexibility is worth the latency cost.

When errors are unacceptable. Agents make mistakes. For tasks where any error is catastrophic—medication dosing, financial transactions above certain thresholds—human oversight is often better than agent autonomy. Agents can assist and recommend; humans can decide.

When the task is too vague. Agents need clear enough objectives to know when they've succeeded. "Improve marketing" is too vague. "Generate five social media post variations for each new blog post" is specific enough. If you can't define success, you can't build an agent that achieves it.

When data doesn't exist. Agents need information to work with. An agent that answers questions about your product needs product documentation. An agent that handles support tickets needs access to customer data. If the data isn't available or accessible, the agent can't function.

The best agents solve problems where AI judgment genuinely adds value, where errors are tolerable, where latency is acceptable, and where the necessary data exists. Not every problem qualifies.

Measuring Agent Success

Vanity metrics are easy—how many tasks the agent processed, how many tokens it consumed. Meaningful metrics are harder.

Task success rate is the fundamental metric. What percentage of tasks did the agent complete correctly? "Correctly" requires definition—did it achieve the objective? Did it avoid errors? This metric should trend upward as the agent improves.

Time to completion matters for user experience. How long from task start to task finish? This includes model latency, tool execution, and any human review steps. Faster isn't always better—thoroughness can justify time—but you should know where time goes.

Escalation rate indicates agent limitations. What percentage of tasks require human intervention? High escalation rates suggest the agent's scope is too broad or its capabilities too limited. Low escalation rates—if legitimate—indicate the agent is handling its domain well.

Cost per task determines economic viability. Include API costs, infrastructure, and any human time. Compare to the cost of doing the task without the agent. The agent should save money or enable tasks that weren't possible before.

User satisfaction is the ultimate metric for customer-facing agents. Surveys, ratings, complaint rates. An agent that's technically successful but frustrates users is failing. Technical metrics are proxies for user value; measure user value directly when possible.

Build dashboards that track these metrics over time. Weekly reviews catch problems before they compound. Monthly reviews assess whether the agent is improving. The agent that isn't measured doesn't improve.

Scaling Agent Operations

As agents prove their value, organizations want to run them at larger scale. Scaling brings new challenges that didn't exist when running a few agents for specific tasks.

Infrastructure for Scale

Queue-based architectures. At scale, agents can't process tasks synchronously. Tasks enter queues; workers consume from queues; results return asynchronously. This decoupling enables scaling workers independently from task generation. Standard message queue technologies—RabbitMQ, SQS, Kafka—work well.

Horizontal scaling. More agents running in parallel. The orchestration layer distributes work across instances. Each instance is stateless; state lives in external stores. When load increases, spin up more instances; when load decreases, scale down. Cloud-native patterns apply directly.

Rate limiting and backpressure. External APIs have rate limits. LLM providers throttle requests. Agents must respect these limits gracefully—queueing work when rate limited, slowing down when approaching limits. Without backpressure handling, agents fail ungracefully under load.

Cost controls at scale. A runaway agent at small scale is annoying. At large scale, it can consume budget in hours. Hard limits per task, per hour, per day. Automatic shutdown when limits approach. Alerting that catches anomalies before they become expensive.

Organizational Scaling

Agent operations as a discipline. Running agents at scale requires dedicated attention—monitoring, maintenance, improvement. This isn't a side project; it's operational responsibility. Organizations that treat agent operations as someone's spare-time job get spare-time results.

Governance and oversight. Which agents can take which actions? Who approves new agent capabilities? What review process exists for agent changes? At scale, governance prevents chaos. Without it, agents proliferate unchecked, each a potential risk.

Incident management for agents. When agents fail at scale, the impact is significant. Runbooks for common failures. On-call rotations that cover agent systems. Post-incident reviews that drive improvements. Agent incidents should be treated with the same seriousness as production service incidents.

Cross-agent coordination. Multiple agents working together need coordination mechanisms—shared state stores, communication protocols, conflict resolution. Without coordination, agents duplicate work, conflict on shared resources, or produce inconsistent outputs. The coordination overhead is real; account for it in architecture and cost planning.

Version management across fleets. Different agent versions in production simultaneously—some customers on the new version, others on stable. Compatibility between versions, data format consistency, and gradual rollout capabilities become essential. What was manual management with a few agents becomes impossible to manage without automation at scale.

Documentation for Agent Systems

Agent behavior must be documented differently than traditional software. The probabilistic nature of LLM outputs means documentation must describe expected behaviors rather than exact outputs.

Runbooks for common failure modes. When the agent starts producing hallucinations, what are the diagnostic steps? When response times spike, what are the likely causes? Document the failure patterns you've seen and how to address them. The on-call engineer at 3 AM shouldn't have to figure things out from scratch.

Prompt documentation with rationale. Prompts aren't self-documenting. Why is this phrase here? What problem was this constraint solving? Without documentation, prompt maintenance becomes archaeology—each change risks undoing past fixes.

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