The gap between "I built a chatbot this weekend" and "I run AI agents in production" is measured in years of hard-won lessons. This isn’t about choosing the right framework or picking the best model. It’s about everything that comes after - the challenges that only reveal themselves when real users interact with your system thousands of times a day.

After years of building agent infrastructure and working with customers deploying agents across every imaginable use case, we’ve accumulated a catalog of problems that no tutorial prepares you for. These are the issues that surface gradually, the architectural decisions that seem trivial until they aren’t, and the edge cases that turn elegant prototypes into maintenance nightmares.

This guide documents what we’ve learned. Not as a framework comparison or a quick start tutorial, but as a map of the territory you’ll traverse if you’re serious about building agents that work.

What follows is a simplified overview that documents some of the key challenges in the most natural way we could. The subject is deep and complex, and we’re not able to provide detailed analysis of each point. However, the information we provide below is 100% real and comes from our own experience - everything has been double-referenced against all of the work we have done in the past 2 years.

Context

Every agent conversation is built on context - the system prompt, the conversation history, the retrieved knowledge, the current user intent. Managing context seems straightforward until you encounter the constraints that make it interesting.

The Context Window Problem

Language models have finite context windows. Modern models offer impressive token limits, but filling them naively creates problems:

• Latency increases linearly with context size. A 128K context window sounds great until you realize that including all available context makes every response take seconds longer.
• Relevance degrades as context grows. More information doesn’t always mean better responses. Models can lose focus when surrounded by marginally relevant content.
• Costs scale directly with token usage. Every extra token in your context is a token you’re paying for, multiplied across thousands of conversations.

The real engineering challenge isn’t fitting everything into context - it’s deciding what to exclude. This requires understanding your use case deeply enough to know what information matters for different types of queries.

Memory and Persistence

Agents that remember things across conversations face immediate questions with no universal answers:

What should be remembered? Not everything in a conversation is worth persisting. User preferences might matter. A clarification about a typo probably doesn’t. Building the heuristics for what to save requires understanding your domain.

How should memories be organized? Flat lists of facts don’t scale. Neither do rigid hierarchies. Real memory systems need searchable, contextual storage with relevance ranking that improves over time.

When should memories be updated versus replaced? Users change their minds. Preferences evolve. A memory system that never updates becomes a liability. One that updates too eagerly loses important historical context.

Who owns the memory? In multi-user or multi-agent scenarios, memory scope becomes critical. Should an agent remember information from one user when talking to another? Usually not, but the boundaries are rarely obvious.

We’ve learned that effective memory systems often need multiple scopes - user-level, session-level, agent-level, and sometimes organization-level - each with different retention policies and access controls.

Conversation History Management

Even within a single conversation, history management presents challenges:

• Summarization versus truncation: When conversations exceed context limits, do you summarize old content (losing specifics) or truncate it (losing chronology)? Both approaches have trade-offs that depend on your use case.
• Message ordering and threading: Real conversations aren’t linear. Users change topics, return to earlier subjects, and interleave multiple threads. Agents that assume linear flow break on actual usage patterns.
• Handling revisions and corrections: When a user says "Actually, I meant to say..." how does that propagate through your context? Simple append-only history doesn’t capture the semantic reality.

Security

The moment you connect an AI agent to real systems, security becomes non-negotiable. The attack surface is larger than most developers initially realize.

Prompt Injection Remains Unsolved

Despite years of attention, prompt injection remains a fundamental challenge. Users - malicious or not - can craft inputs that cause your agent to behave unexpectedly:

• Direct injection through user inputs that override system instructions
• Indirect injection via content the agent retrieves or processes from external sources
• Multi-turn attacks that gradually shift the agent’s behavior across conversation turns

Defense is layered: input validation, output filtering, behavioral monitoring, and architectural isolation. No single technique is sufficient. The agents that handle this best treat every boundary as a potential injection point.

Secrets and Credentials

Agents that act on behalf of users require credentials. Managing these credentials safely is harder than it appears:

• Storage: Where do OAuth tokens, API keys, and user credentials live? Encrypted at rest is table stakes. Access logging and rotation policies matter more.
• Scope: An agent with a user’s Slack token shouldn’t necessarily have access to their email. Least-privilege principles apply, but implementing fine-grained scoping requires significant architectural investment.
• Expiration and refresh: Tokens expire. Refresh flows fail. Your agent needs graceful handling for credential issues without exposing details that help attackers.
• Revocation propagation: When a user revokes access, how quickly does your system stop using their credentials? Minutes matter when trust is broken.

We’ve found that secrets management often requires a dedicated subsystem - not an afterthought bolted onto the main agent logic.

Data Scope and Access Control

Multi-tenant systems face questions about data visibility:

• If an agent can search a knowledge base, which documents should it see?
• If an agent can create resources, who owns them?
• If an agent references another user’s content, what privacy implications follow?

Scoping rules seem simple until you encounter shared resources, delegated access, and organizational hierarchies. The filtering logic can become surprisingly complex - and getting it wrong means either data leakage or mysterious failures when agents can’t access resources they should.

Input Validation at Every Boundary

Agents receive input from multiple sources: users, APIs, retrieved content, tool outputs. Each boundary requires validation:

• Schema validation ensures structural correctness
• Semantic validation catches meaningful but incorrect inputs
• Rate limiting prevents abuse
• Size limits prevent resource exhaustion

The validation logic often needs to be domain-specific. A valid JSON payload might still contain SQL injection attempts, prompt injection vectors, or simply malformed data that will break downstream processing.

Integrations

Agents become useful when they connect to real systems. This connection point is where most complexity accumulates.

OAuth and Authentication Flows

Integrating with third-party services means dealing with their authentication requirements:

• OAuth 2.0 variations: Every provider implements OAuth slightly differently. Scopes, token formats, refresh behavior, and error responses vary in ways that documentation often doesn’t capture.
• Token lifecycle: Access tokens expire. Refresh tokens can be revoked. Your agent needs to handle mid-conversation auth failures gracefully.
• Consent and re-consent: Users grant permissions, revoke them, and sometimes need to re-authorize. The UI and flow design for these moments affects user trust significantly.

We’ve learned to treat auth integration as a first-class concern, not a library call. The edge cases are numerous and the failure modes are user-visible.

API Rate Limits and Quotas

Every external API has limits. Your agent’s interaction patterns can hit these limits in unexpected ways:

• Burst versus sustained limits: An agent processing a queue might stay under sustained limits while exceeding burst limits.
• Shared limits across operations: One API call might consume quota that affects unrelated operations.
• Undocumented limits: Not all limits are documented. Some emerge only under load.

Effective rate limit handling requires queuing, backoff strategies, and graceful degradation. When you can’t call an API, what does the agent do instead?

Error Handling in Distributed Systems

When your agent calls external services, failures are inevitable:

• Network failures: Timeouts, connection drops, and DNS issues
• Service failures: 5xx errors, maintenance windows, and cascading failures
• Semantic failures: Success responses with empty or incorrect data

Each failure type requires different handling. Retry logic for a timeout might be wrong for a rate limit. Surfacing an error to users might be right for a payment failure but wrong for a temporary backend hiccup.

Webhook and Event Handling

Agents that respond to external events (new messages, state changes, scheduled triggers) need robust event handling:

• Idempotency: Events may be delivered multiple times. Your handling needs to be safe for repeated execution.
• Ordering: Events may arrive out of order. Assuming chronological delivery breaks on real systems.
• Backpressure: Event volumes spike. Your system needs to queue, batch, or shed load gracefully.

Stalled event processing can leave agents in inconsistent states. We’ve built systems to detect and recover from processing failures - because they will happen.

State

Agents maintain state at multiple levels, and managing this state correctly is deceptively difficult.

Conversation State

Beyond simple message history, conversations carry state:

• User preferences expressed during the conversation
• Task progress for multi-step operations
• Context references ("as I mentioned earlier")
• Emotional tone and rapport established

This state is often implicit in the conversation history, but relying on the model to extract it reliably every time adds latency and inconsistency. Explicit state tracking for important properties improves reliability.

Agent State

Agents themselves may have state that persists across conversations:

• Learned preferences about how to interact with this user
• Pending tasks that span multiple sessions
• Accumulated knowledge about the user’s domain

Managing this state requires decisions about storage, synchronization (for distributed systems), and lifecycle (when does state become stale?).

Resource State

When agents create or modify external resources, state consistency becomes critical:

• Transaction semantics: If an agent creates a calendar event and then fails to send a confirmation, what’s the correct state?
• Conflict resolution: What happens when an agent and a user modify the same resource concurrently?
• Rollback strategies: Can operations be undone? How far back?

These questions have different answers depending on the integrations involved, and building robust handling for each is significant engineering effort.

Actions and Tool Use

Modern agents use tools - functions they can call to take actions or retrieve information. Tool design profoundly affects agent behavior.

Tool Definition Quality

How you describe a tool to the model matters enormously:

• Clear parameter descriptions reduce hallucinated or malformed inputs
• Examples in descriptions help models understand expected usage patterns
• Explicit constraints (required fields, valid ranges) prevent frustrating errors
• Action consequences (especially for destructive operations) help models decide when to use tools

We’ve found that investing in tool documentation pays dividends in agent reliability. A tool the model understands well will be used correctly more often.

Tool Selection and Routing

When agents have access to many tools, selection becomes a challenge:

• Too many options overwhelms the model’s decision-making
• Similar tools cause confusion and inconsistent selection
• Missing tools for edge cases leads to incorrect improvisation

Structuring tools into logical groups, providing selection guidance in system prompts, and monitoring actual usage patterns helps refine tool sets over time.

Tool Output Processing

What tools return matters as much as what they do:

• Structured outputs are easier for agents to interpret correctly
• Error information should be actionable, not cryptic
• Partial successes need clear communication
• Size limits prevent tool outputs from overwhelming context

Tool outputs become part of the agent’s context. Designing them for model consumption - not just human readability - improves downstream responses.

Instruction Parsing

When agents need to execute structured actions based on natural language, parsing becomes critical:

• Field extraction from conversational context
• Default value handling for optional parameters
• Type coercion from string inputs to proper types
• Validation feedback that helps users correct malformed requests

Building robust parsing that handles the variety of ways users express the same intent requires ongoing refinement based on real usage patterns.

Observability

AI agents are notoriously difficult to debug. Building observability from the start is essential.

Logging and Tracing

Effective agent logs capture:

• Full prompt construction including system prompt, context, and user input
• Model responses with token usage and latency
• Tool calls and results including external API interactions
• Decision points where the agent chose between alternatives

Tracing across async operations and external services helps diagnose issues that span multiple systems.

Activity Tracking

Beyond technical logs, tracking semantic activity helps understand agent behavior:

• Conversation patterns: What paths do users take through conversations?
• Tool usage distribution: Which tools are used most? Which are never used?
• Error patterns: Are certain user requests consistently failing?
• Success metrics: What does a "good" conversation look like?

This higher-level view reveals issues that low-level logs obscure.

Error Classification

Not all errors are equal:

• User errors (unclear requests, missing information)
• Agent errors (misunderstanding, wrong tool selection)
• System errors (downstream failures, resource exhaustion)
• Model errors (hallucinations, instruction violations)

Classifying errors helps prioritize fixes and measure improvement over time.

Feedback Loops

The best agents improve from their mistakes:

• User feedback (explicit ratings, implicit signals)
• Human review of sampled conversations
• Automated evaluation against expected behaviors
• A/B testing of prompts and tool configurations

Building infrastructure for continuous improvement is as important as the initial implementation.

Performance and Cost

Production systems face real constraints on speed and cost that prototype systems ignore.

Latency Budgets

Users have expectations about response time:

• Initial response time: How long before anything appears?
• Streaming behavior: Does output appear incrementally?
• Tool call overhead: How much do external calls add?
• Total conversation time: How long does a complete interaction take?

Meeting latency expectations often requires sacrificing completeness or accuracy. Understanding these trade-offs for your specific use case is essential.

Token Economics

Token usage translates directly to cost:

• Model selection trades capability for cost
• Context management affects both input and output tokens
• Caching strategies reduce redundant processing
• Batching opportunities improve throughput efficiency

For high-volume applications, small efficiency improvements compound into significant cost savings.

Resource Scaling

Agents under load reveal scaling constraints:

• Connection pools to databases and external services
• Memory usage for conversation state and caching
• CPU utilization for parsing and validation
• Queue depths for async processing

Identifying bottlenecks requires load testing with realistic usage patterns - not just synthetic benchmarks.

User Experience

Ultimately, agents exist to help humans. The user experience layer is where technical capability becomes value.

Error Recovery

When things go wrong - and they will - the user’s experience of the failure matters:

• Clear communication about what went wrong
• Actionable guidance for how to proceed
• Graceful degradation when full functionality is unavailable
• State preservation so users don’t lose progress

The difference between a frustrating failure and an acceptable one often comes down to how it’s communicated.

Human Handoff

Agents shouldn’t try to handle everything:

• Recognition of when human intervention is needed
• Smooth transitions that preserve context
• Escalation tracking to measure agent limitations
• Learning from handoffs to expand capability over time

Building effective handoff requires clear criteria for escalation and infrastructure to support it.

Trust Calibration

Users need accurate mental models of what agents can do:

• Capability communication that sets appropriate expectations
• Confidence indication for uncertain responses
• Limitation acknowledgment that builds rather than undermines trust
• Consistent personality that users can learn to predict

Agents that accurately represent their capabilities earn trust. Agents that overcommit and underdeliver lose it.

Multi-Channel Consistency

Agents deployed across multiple channels (web, mobile, messaging platforms, voice) need consistent behavior:

• Channel-appropriate formatting and interaction patterns
• State synchronization across channels
• Capability parity (or clear communication of differences)
• Identity consistency so users recognize the same agent

Each channel has constraints and conventions. Respecting them while maintaining a coherent agent identity requires careful design.

The Long View

Building production AI agents is an ongoing process, not a destination. The systems that succeed share some characteristics:

They start simple and expand carefully. Every new capability is a new maintenance burden. Adding features is easy; removing them is hard.

They instrument everything. You can’t improve what you can’t measure. Observability investments pay off across the entire system lifetime.

They design for change. Models improve, APIs change, user expectations evolve. Systems that assume stability become liabilities.

They treat failure as normal. Resilient systems don’t assume everything works. They handle failures gracefully and recover automatically when possible.

They stay close to users. Real usage patterns reveal problems that no amount of theoretical design catches. Feedback loops matter.

The gap between a weekend project and a production system isn’t primarily technical knowledge - it’s accumulated experience with the edge cases, failure modes, and design trade-offs that only reveal themselves over time and at scale. This guide captures some of what we’ve learned. Your journey will reveal more.

The frameworks will keep getting better. The models will keep improving. But the fundamental challenges of building systems that work reliably in the real world - context, security, integration, state, observability, performance, and user experience - will remain. Understanding these challenges is the real foundation for building agents that matter.