The Agentic Mesh: How AI Agents Actually Work
Understanding the fundamental loop, memory architecture, tool use, and multi-agent orchestration - with every failure mode mapped to its ADHD parallel.
When people imagine AI agents, they often picture something like a digital employee--a system that receives instructions, thinks through the problem, and delivers results. The reality is both simpler and more interesting.
AI agents aren't independent intelligences. They're orchestrated loops of prediction and action, built on language models that generate probable text given their context. Understanding how they actually work reveals why the infrastructure patterns we've discussed matter so much.
The Fundamental Loop
Every AI agent, regardless of complexity, runs the same basic cycle:
- Receive context: The agent gets a prompt containing instructions, history, and relevant information
- Generate a response: The language model predicts what comes next
- Execute any actions: If the response includes tool calls, those get executed
- Loop: Results from actions feed back into context for the next iteration
This is the agentic loop. It repeats until the task is complete, an error occurs, or a limit is reached.
What makes this loop powerful isn't the intelligence of any single step--it's the ability to take action in the world and incorporate the results. The agent can read files, search the web, execute code, call APIs. Each action generates information that informs the next prediction.
Context as Compiled View
The critical insight that separates systems that work from systems that fail:
Context is not a chat transcript. It's a compiled view over richer underlying state.
The naive approach treats context as a growing buffer. Every message, every tool call, every result gets appended. Eventually the buffer overflows or attention degrades so badly that the agent stops making sense.
The correct approach treats each agent call as a fresh compilation. What's relevant NOW? What instructions apply NOW? Which artifacts matter NOW? The underlying state can be enormous--every decision ever made, every file in the codebase, every message in the conversation. But the working context is computed fresh for each step, containing only what's necessary.
This is the same pattern I described for knowledge graphs: query the full corpus, but only surface what's relevant to the current question. It's also the pattern that compensates for ADHD working memory: maintain rich external systems, but only load what you need into limited cognitive bandwidth.
The Four-Layer Memory Architecture
Production-scale AI systems use a tiered memory architecture that maps remarkably well to how we should think about human knowledge management:
Layer 1: Working Context
What actually gets sent to the model on each call. This layer is:
- Computed, not accumulated: Assembled fresh for each step
- Minimal: Only what's necessary for the current decision
- High-signal: Every token must justify its attention cost
Think of this as active working memory--what you're holding in your head right now.
Layer 2: Sessions
Structured event logs capturing the full trajectory within a single interaction. This includes user messages, agent replies, tool calls, results, errors, and state transitions.
The critical insight: sessions store structured records, not raw prompt strings. This makes them model-agnostic (you can swap models without rewriting history), processable (downstream operations work over typed events), and observable (you can inspect precise state transitions).
Think of this as short-term memory--your recollection of what happened in the current work session.
Layer 3: Memory
Searchable knowledge the agent queries on demand. This includes cross-session insights, extracted patterns, and learned strategies.
Critical property: memory is RETRIEVED when relevant, not permanently present. It's accessed through search, not through always-on context loading.
Think of this as long-term memory--the vast store of experience that you recall when prompted, but don't hold in active awareness.
Layer 4: Artifacts
Large objects stored by reference: codebases, documents, database results, tool outputs. These are accessed through pointers, not tokenized into context.
Think of this as external reference material--books on your shelf, files on your computer, information you know exists and can access but don't carry in your head.
Tool Use: Extending Agent Capabilities
The most powerful feature of modern AI agents is tool use--the ability to take actions beyond generating text.
A tool is a function the agent can call. It has a name, a description of what it does, and a specification of what inputs it accepts. When the agent decides to use a tool, it generates a structured call with the appropriate parameters. The system executes the tool and returns results that feed into the next context compilation.
Tools transform agents from text generators into general-purpose problem solvers:
- File tools let agents read and write documents
- Search tools let agents query knowledge bases and the web
- Execution tools let agents run code and observe outputs
- API tools let agents interact with external services
The agent doesn't actually execute tools--it decides which tools to call and with what arguments. The orchestration system handles execution and feeds results back.
This separation is important. The agent is making predictions about what action would be helpful. The system is actually taking the action. Errors, permissions, and side effects are all managed by the orchestration layer, not by the model itself.
Multi-Agent Orchestration
Complex tasks often benefit from multiple specialized agents working together rather than one general agent trying to do everything.
Consider a software development task: understanding requirements, planning implementation, writing code, running tests, fixing bugs. Each step benefits from different context and different instructions. A planner agent might have architectural principles and project history. A coding agent might have relevant source files and style guides. A testing agent might have test frameworks and failure patterns.
Multi-agent orchestration involves:
Routing: Determining which agent handles which subtask. This can be rule-based (certain keywords trigger certain agents) or model-based (a router agent decides).
Handoffs: Passing relevant context from one agent to another without overwhelming the recipient. The outgoing agent summarizes what was accomplished; the incoming agent gets fresh instructions plus the summary.
Coordination: Managing shared resources, preventing conflicts, and combining results. If two agents modify the same file, how are changes merged? If agents disagree, how is the conflict resolved?
The biological metaphor I use for my own systems: agents as specialized cells in an organism. Each cell has its own function, but they communicate through defined interfaces and contribute to the health of the whole.
Why Agents Fail: The Nine Failure Modes
Understanding how agents fail is essential for building systems that work. Research has identified nine common failure modes:
1. Context Overflow: The agent tries to maintain too much information, exceeding limits or degrading attention quality.
2. Lost in the Middle: Information placed in the middle of long contexts gets ignored--the model attends to the beginning and end.
3. Instruction Decay: Over long runs, the agent drifts from original instructions as recent context dominates attention.
4. Tool Confusion: The agent calls wrong tools, passes wrong parameters, or misinterprets results.
5. Loop Traps: The agent repeats the same failing approach, unable to break out and try something different.
6. Hallucination Cascades: One hallucinated fact gets incorporated into context and compounds through subsequent steps.
7. Goal Drift: The agent loses track of the original objective, optimizing for intermediate metrics instead.
8. Permission Failures: The agent attempts actions it doesn't have permission to take, wasting cycles on doomed approaches.
9. Recovery Failures: When errors occur, the agent can't recover gracefully and either crashes or produces garbage.
Each failure has a mitigation pattern. Context overflow: aggressive summarization and retrieval-based memory. Lost in the middle: strategic placement of critical information. Instruction decay: pinned context that doesn't get rotated out.
The ADHD Parallel: Failure Mode Mapping
Every single failure mode that AI agents experience maps to something ADHD brains experience:
| Agent Failure | ADHD Parallel |
|---|---|
| Context overflow | Working memory overload |
| Lost in the middle | Forgetting instructions mid-task |
| Instruction decay | Losing track of the original goal |
| Tool confusion | Starting the wrong approach |
| Loop traps | Doing the same ineffective thing repeatedly |
| Hallucination cascades | False memories compounding |
| Goal drift | Getting distracted by tangents |
| Permission failures | Attempting things you can't actually do |
| Recovery failures | Struggling to restart after interruption |
The solutions map too:
- Aggressive summarization = Writing things down immediately
- Strategic placement = Keeping critical information visible
- Pinned context = Persistent environmental cues
- Structured handoffs = Clear transition rituals between tasks
Both AI agents and ADHD brains face fundamental constraints on working memory and attention. Both benefit from the same architectural patterns: external memory, computed context, tiered retrieval.
Building Agent-Friendly Infrastructure
If you're building systems that AI agents will use--or that you'll use in agent-like ways--certain design principles help:
Structured outputs: Make information machine-readable. JSON, markdown with consistent headings, clear metadata. The easier it is to parse, the more useful it is as agent context.
Explicit state: Don't rely on implied context. State what's true, what's been tried, what's remaining. Agents (and ADHD brains) benefit from external state representation.
Clear boundaries: Define what's in scope and out of scope. Agents work better with constrained action spaces. Humans do too.
Observable actions: Log what happens. When things fail, the ability to replay and understand is essential for both debugging and learning.
Graceful degradation: Plan for partial failures. When one approach doesn't work, make it easy to try another.
The Infrastructure Convergence
The more I study how AI agents work, the more I recognize patterns I invented independently for managing my own cognition.
The four-layer memory architecture? That's my knowledge graph (artifacts), searchable decision logs (memory), work session notes (sessions), and what I'm actively thinking about (working context).
Tool use with structured interfaces? That's templates and checklists--defined procedures I invoke with specific parameters.
Multi-agent orchestration? That's delegating specialized tasks to systems (or people) optimized for those tasks, with clear handoffs and defined interfaces.
The infrastructure patterns that make AI agents work at scale are the same patterns that make neurodivergent brains work effectively. Not because one copied the other, but because both face similar constraints and those constraints have optimal solutions.
Go Deeper
Explore related deep-dives from the Constellation Clusters:
- Context Windows as Working Memory - The constraint that defines AI agent architecture
- Orchestration Patterns - Multi-agent coordination strategies for complex workflows
- Memory and State Management - The four-layer architecture for agent memory