Understanding AI Agents

Imagine hiring an analyst who can summarize any document, answer complex questions, and provide insightful recommendations—but who cannot pick up a phone, send an email, or access any system beyond the papers on their desk. This is essentially what Large Language Models (LLMs) are: incredibly capable text processors that exist in isolation.

AI agents solve this fundamental limitation by giving LLMs the ability to interact with the world.

LLMs that can analyze customer complaints but can't access the customer database. Agent can use LLMs to analyze complaints and look up the customer's history and update their account.

The Core Constraint

Large Language Models receive text and return text. That's it. They cannot:

• Access the internet or external data sources
• Send emails or notifications
• Execute code or scripts
• Remember conversations across sessions
• Take any action beyond generating text

This constraint exists by design. LLMs are trained to be predictable text transformers, not autonomous systems. But this limitation also means that despite their impressive reasoning capabilities, LLMs alone cannot solve real-world problems that require action.

Agent Emergence

Agents wrap LLMs with the infrastructure needed to:

1. Provide context - Feed relevant data to the LLM
2. Enable action - Execute the LLM's recommendations
3. Maintain state - Remember previous interactions
4. Ensure safety - Control what actions are permitted

Several technological shifts converged to make AI agents viable in 2024-2025.

1. Large language models achieved reliable reasoning capabilities, moving beyond pattern matching to genuine problem-solving.
2. Tool-calling APIs allowed LLMs to interact with external systems more reliably.
3. Vector databases and retrieval systems made it more practical to give agents long-term, searchable memory.
4. The cost of LLM inference dropped from prohibitively expensive to affordable for many applications.

The Three Fundamental Patterns

AI agents cluster around three core patterns, each solving different organizational problems.

Memory Builders

Memory builders continuously observe and synthesize information, creating knowledge that becomes more valuable over time. Unlike traditional databases that store static facts, these agents build evolving understanding.

User and persona memory agents watch how people communicate and work, building rich profiles that inform future interactions:

• A Slack bot that learns each team member's communication style, preferred meeting times, and expertise areas, then facilitates better introductions and scheduling
• A learning coach that tracks not just what you study, but how you learn best—whether you prefer visual explanations, need more practice problems, or learn better in shorter sessions

Project knowledge base agents transform scattered information into living documentation:

• Systems that automatically harvest design docs, PR descriptions, and demo recordings, then maintain a continuously updated project specification with cross-references and dependency tracking
• Agents that detect requirements drift by comparing user stories against README files and code comments, opening corrective tickets when they diverge

Dynamic competitor intelligence provides the strategic awareness that human analysts struggle to maintain consistently:

• Monitoring competitor job postings, product updates, and news coverage, then maintaining evolving profiles that highlight strategic shifts and competitive threats

The power of memory builders lies in their compound effect. Each observation makes the system smarter, creating feedback loops that human-maintained systems can't sustain.

Reactive Workflow Starters

Reactive agents embed reasoning directly into event-driven systems. They don't just respond to triggers—they interpret them and choose appropriate responses.

Customer support automation that goes beyond keyword matching:

• When a support ticket arrives with negative sentiment, the agent considers the customer's history, the technical context, and the company's current priorities to craft empathetic responses and route escalations appropriately
• Systems that correlate customer complaints with server logs, automatically creating engineering tickets when user issues indicate systemic problems

Development workflow intelligence that understands code and process context:

• CI failure agents that analyze failing diffs, compare against historical patterns, and suggest fixes—or automatically deprioritize known flaky tests
• Code review assistants that understand project conventions and can distinguish between stylistic preferences and potential bugs

Sales process automation that reads buyer signals:

• Email engagement tracking that generates contextual follow-ups based on which assets prospects viewed and how long they spent reading
• Lead scoring that considers not just demographic data but behavioral patterns and communication tone

Periodic Sweepers

Sweeper agents handle the ongoing maintenance tasks that organizations struggle to do consistently. They bring reasoning to routine operations.

Continuous operations management:

• Hourly review of support backlogs that can identify duplicate issues, recognize VIP customers, and escalate based on business context rather than just technical severity
• Security log analysis that distinguishes between normal operational noise and genuine anomalies, providing security teams with actionable intelligence rather than alert fatigue

Knowledge synthesis and compliance:

• Nightly re-indexing of internal documentation that extracts key Q&A pairs and maintains organizational knowledge graphs
• Monthly compliance evidence compilation that assembles audit trails by understanding what auditors actually need rather than just collecting everything

The advantage of periodic sweepers is their reliability—they never forget to run, never have "off days," and consistently apply the same quality of judgment to routine tasks.

Why These Patterns Work

Context accumulation creates compound value. Traditional automation systems start fresh each time they run. AI agents build understanding over time, making each interaction more informed than the last. This explains why memory builders often provide the highest ROI—their value increases exponentially.

Judgment calls are where AI agents excel. Rules-based systems fail when situations don't match predefined categories. AI agents can reason about edge cases, weigh competing priorities, and make decisions that "feel right" even in novel situations.

Human-AI collaboration beats full automation. The most successful agent implementations preserve human oversight for high-stakes decisions while automating the judgment-heavy grunt work that humans find tedious. This hybrid approach captures most of the efficiency gains while maintaining appropriate control.

When Agents Fail

Common failure modes include:

Context Overload: LLMs can become confused when given too much irrelevant information. Solution: Implement smart context filtering and relevance scoring.

Tool Integration Errors: APIs fail, databases are temporarily unavailable, or permissions change. Solution: Robust error handling with graceful degradation and retry logic.

Reasoning Errors: LLMs make logical mistakes or miss important nuances. Solution: Implement validation checks and confidence scoring for agent decisions.

Scope Creep: Agents gradually take on tasks they weren't designed for, leading to unexpected behaviors. Solution: Clear boundaries and regular audits of agent activities.

How Agents Work

An agent system typically follows this flow:

1. Input Processing - Receive a request or trigger
2. Context Gathering - Collect relevant information for the LLM
3. LLM Reasoning - Generate analysis and action recommendations
4. Action Execution - Perform the recommended actions using available tools
5. Result Validation - Verify outcomes and handle errors
6. Response Generation - Provide feedback to the user

The LLM remains the "brain" of the system, but the agent provides the "hands and feet" needed to interact with external systems.

Agent Capabilities

Modern agents typically provide five core capabilities.

Evaluate

The LLM analyzes information and makes decisions. For example, given a financial transaction with a purchase location 3,000 miles from the user's typical spending pattern, the amount exceeds their usual transaction size by 10x, and it occurred at 3 AM local time and determining "This appears fraudulent."

Act

The agent executes actions based on LLM recommendations. This might involve calling APIs, updating databases, sending notifications, or triggering other systems. The LLM decides what to do, while the agent handles how to do it.

Iterate

Agents can break complex tasks into steps, calling the LLM multiple times or coordinating with other agents. For instance, processing a customer refund might require checking order history, validating return policies, calculating refund amounts, and updating multiple systems—each step informed by LLM reasoning.

Govern

Agents implement guardrails and constraints around LLM actions. This includes spending limits, approval workflows, data access controls, and safety checks. The LLM might recommend transferring $10,000, but the agent ensures this doesn't exceed authorized limits.

Orchestrate

Agents manage workflows, coordinate between systems, and maintain conversation memory. They handle the operational complexity that allows LLMs to focus on reasoning rather than infrastructure concerns.

Alternative Approaches and When to Choose Them

Traditional rule-based automation remains superior for high-volume, low-ambiguity tasks. If you can write clear if-then rules that cover 95% of cases, conventional automation is faster and cheaper.

Human-only processes are still necessary for tasks requiring creativity, relationship-building, or handling of truly novel situations. AI agents augment rather than replace human judgment for the most complex decisions.

Hybrid systems often provide the best of both worlds—AI agents handle the nuanced middle ground while simple rules catch obvious cases and humans handle the most complex edge cases.

The Broader Context

The most successful early adopters aren't trying to replace human workers wholesale. Instead, they're identifying the mundane-but-complex tasks that drain human energy and creativity, then deploying AI agents to handle these systematically. This frees humans to focus on genuinely creative and strategic work.

A conventional system might automatically assign support tickets based on keywords. An AI agent reads the ticket, classifies the customer's emotional state, recognizes that they're a high-value client having their third issue this month, and crafts a response that acknowledges their frustration while escalating appropriately. The agent doesn't just follow rules—it exercises judgment.

For decades, automation meant writing explicit rules: "if this happens, do that." AI agents represent something fundamentally different—systems that can reason about context, make judgment calls, and improve their understanding over time.

See also

• Stevens: a hackable AI assistant using a single SQLite table and a handful of cron jobs
• What can agents actually do?
• A Survey of Context Engineering for Large Language Models
• AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security