Design and build autonomous AI agents that can plan, reason, and take action. Covers agent loops, tool use, memory systems, multi-agent orchestration, guardrails, and the patterns that make AI agents reliable and controllable.
AI Agent Architecture defines the structural and operational design of autonomous systems that perceive, reason, act, and learn in complex, dynamic environments. It matters when an AI system must make decisions over time with minimal human intervention, such as in real-time customer support, autonomous data processing pipelines, or continuous software deployment. This is not about a single model inference; it is about orchestrating multiple model components, stateful execution, tool use, and feedback loops across time and services.
An AI agent is composed of five interdependent components: perception, reasoning, planning, execution, and memory. These form a feedback loop where the agent observes its environment, reasons about goals, plans actions, executes them via tools, and updates its internal state.
Perception transforms raw data into structured, agent-ready representations. Inputs may be text, audio, video, sensor streams, or API responses. The agent must normalize, chunk, and embed these inputs to enable reasoning.
Input streams are typically ingested via message queues (Kafka, RabbitMQ) or HTTP APIs. A common pattern uses a POST /api/agent/incoming endpoint that accepts JSON payloads with a source, timestamp, and data field.
{
"source": "customer-support-chat",
"timestamp": "2024-04-05T14:32:10Z",
"data": {
"user_id": "u12345",
"messages": [
{ "role": "user", "content": "My order #78912 is delayed." },
{ "role": "assistant", "content": "I’ll check that for you." }
]
}
}The agent processes this input through a preprocessing pipeline. This includes text normalization (lowercasing, tokenization, lemmatization), audio transcription (via Whisper), and embedding using a model like text-embedding-3-small.
A transformer service runs on a Kubernetes cluster, triggered by kubectl apply -f transformer-deployment.yaml. The deployment includes an env section defining:
env:
- name: EMBEDDING_MODEL
value: "text-embedding-3-small"
- name: CHUNK_SIZE
value: "512"
- name: CHUNK_OVERLAP
value: "128"
- name: NORMALIZE_TEXT
value: "true"Chunks are stored in a vector database (Pinecone, Weaviate, or a raw PostgreSQL table with vector type). The agent queries these using a SELECT * FROM chunks WHERE embedding <#> ? ORDER BY distance LIMIT 5 statement.
Reasoning is the agent’s “brain.” It selects, composes, and applies models to interpret the current state and generate high-level actions.
The agent uses a reasoning engine that runs a sequence of model calls. Each call is described by a task object with a model, prompt, and input_fields.
{
"task": "classify_intent",
"model": "gpt-4o-mini",
"prompt": "Classify the intent of the user message. Return only JSON with 'intent' and 'confidence'.",
"input_fields": ["messages.last.content"]
}This task is sent to a reasoning-service via HTTP. The service maintains a tasks table:
CREATE TABLE tasks (
id UUID PRIMARY KEY,
agent_id VARCHAR(64),
status VARCHAR(16) DEFAULT 'pending',
input JSONB,
output JSONB,
created_at TIMESTAMPTZ,
started_at TIMESTAMPTZ,
completed_at TIMESTAMPTZ
);The service polls for pending tasks using SELECT * FROM tasks WHERE status = 'pending' ORDER BY created_at LIMIT 10 FOR UPDATE SKIP LOCKED.
The agent’s reasoning engine applies a chain-of-thought pattern. It performs sequential reasoning steps, each producing a new output that becomes input to the next.
[
{
"step": "identify_customer",
"model": "gpt-4o",
"prompt": "Extract customer name, ID, and loyalty tier from the conversation.",
"input": "messages"
},
{
"step": "diagnose_issue",
"model": "gpt-4o-mini",
"prompt": "Given the customer context, diagnose the most likely cause of the delay.",
"input": ["messages", "customer"]
},
{
"step": "generate_response",
"model": "gpt-4o",
"prompt": "Write a personalized response to the customer, including empathy and next steps.",
"input": ["messages", "diagnosis"]
}
]Each step is executed asynchronously. The agent tracks progress via status updates: pending, running, completed, failed.
Planning defines the sequence of actions the agent will execute. It is the difference between reacting to a prompt and proactively managing a workflow.
The agent uses a planning engine that generates a plan object from a high-level goal.
{
"goal": "Resolve customer order delay issue",
"plan": [
{
"action": "fetch_order_details",
"tool": "order-api",
"parameters": { "order_id": "78912" },
"dependencies": []
},
{
"action": "check_shipment_tracking",
"tool": "tracking-service",
"parameters": { "order_id": "78912" },
"dependencies": ["fetch_order_details"]
},
{
"action": "send_customer_email",
"tool": "email-service",
"parameters": {
"to": "[email protected]",
"subject": "Your order is delayed",
"body": "We found your order #78912 is delayed..."
},
"dependencies": ["check_shipment_tracking"]
}
]
}Plans are stored in a plans table:
CREATE TABLE plans (
id UUID PRIMARY KEY,
agent_id VARCHAR(64),
goal TEXT,
status VARCHAR(16) DEFAULT 'draft',
steps JSONB,
created_at TIMESTAMPTZ
);The agent executes plans by scheduling tasks in a queue. Each step is processed by a worker that:
A key failure mode is plan drift: the agent creates a plan, but the environment changes before execution. A plan may become stale if the order-api returns updated data during the execution of check_shipment_tracking.
Execution is the agent’s ability to interact with external systems. Tools are defined by a tool registry, each with a name, endpoint, input_schema, and output_schema.
A tool called order-api is registered as:
{
"name": "order-api",
"endpoint": "https://api.example.com/orders/{order_id}",
"method": "GET",
"input_schema": {
"type": "object",
"properties": {
"order_id": { "type": "string" }
}
},
"output_schema": {
"type": "object",
"properties": {
"order_id": { "type": "string" },
"status": { "type": "string" },
"tracking_number": { "type": "string" },
"estimated_delivery": { "type": "string" }
}
}
}The agent calls a tool using a tool_call message:
{
"tool": "order-api",
"parameters": { "order_id": "78912" },
"task_id": "t12345"
}The tool runner executes the HTTP request and returns:
{
"tool": "order-api",
"result": {
"order_id": "78912",
"status": "shipped",
"tracking_number": "TRK-98765",
"estimated_delivery": "2024-04-12"
},
"timestamp": "2024-04-05T14:33:05Z"
}The agent logs tool calls in a tool_calls table:
CREATE TABLE tool_calls (
id UUID PRIMARY KEY,
agent_id VARCHAR(64),
tool_name VARCHAR(64),
parameters JSONB,
result JSONB,
status VARCHAR(16),
started_at TIMESTAMPTZ,
completed_at TIMESTAMPTZ
);A silent failure occurs when a tool returns result but omits status. The agent assumes success, but the tool may have failed. The default status is pending, so a missing status field in the response leads to a subtle, hard-to-diagnose error: the agent continues without waiting for a completed signal.
Memory enables the agent to maintain context across sessions and over time. It is not just a cache but a persistent, structured store.
The agent uses two types of memory: short-term (session) and long-term (persistent).
Short-term memory is stored in a key-value store (Redis) with a TTL of 30 minutes. Each agent session has a session_id and stores variables like:
{
"session_id": "s98765",
"context": {
"current_intent": "order_delay",
"customer": {
"name": "Alice Johnson",
"loyalty_tier": "gold"
},
"last_plan": "p12345"
},
"history": [
{ "step": "fetch_order_details", "status": "completed" },
{ "step": "check_shipment_tracking", "status": "running" }
]
}Long-term memory is stored in a memory table:
CREATE TABLE memory (
id UUID PRIMARY KEY,
agent_id VARCHAR(64),
type VARCHAR(32), -- "fact", "event", "observation", "decision"
content JSONB,
metadata JSONB,
created_at TIMESTAMPTZ,
expires_at TIMESTAMPTZ
);The agent can query memory using a SELECT * FROM memory WHERE type = 'fact' AND created_at > '2024-04-01'.
A common mistake is conflating memory state with execution state. The agent may update a context object in Redis but fail to persist it to the memory table. When the agent restarts, it restores the session, but the long-term memory is inconsistent.
FOR UPDATE SKIP LOCKED not being atomic across services. The agent executes the same plan twice, leading to duplicate emails or order updates.completed. The agent assumes readiness but the data is incomplete. The solution is to require a completed_at timestamp on the dependency task.gpt-4o for reasoning, but the model has changed. The input_schema and output_schema are not versioned. The agent fails silently when the model outputs confidence_score instead of confidence.content field is not indexed. Queries take 500ms on 100k records, and the agent becomes sluggish under load.timeout: 5000 in the tool_call request, but the tool does not return a timeout field in the response. The agent assumes a success but the task is still running.chunk_overlap of 128 tokens and to use a merge_strategy of concatenate or mean_pooling.The agent orchestrates these components through a central execution loop. It runs in a main-agent-loop service that:
task for each message.This loop is configured via environment variables:
export AGENT_NAME="customer-support-agent"
export TASK_QUEUE="kafka://agent-queue"
export REASONING_MODEL="gpt-4o-mini"
export MAX_CHUNK_SIZE=512
export MEMORY_TTL="30d"
export PLAN_RETRY_COUNT=3
export TOOL_TIMEOUT=5000The agent starts with systemctl start ai-agent and logs to /var/log/ai-agent/main.log, which includes structured JSON entries:
{
"timestamp": "2024-04-05T14:32:10Z",
"level": "INFO",
"event": "task_started",
"task_id": "t12345",
"agent_id": "support-agent-01",
"source": "customer-support-chat"
}The agent fails silently when the systemctl unit fails to start, but the ai-agent binary is not registered in /etc/systemd/system/ai-agent.service. The ExecStart command must be ExecStart=/usr/bin/ai-agent --config=/etc/ai-agent/config.yaml.
This architecture enables agents to evolve from reactive bots to proactive, self-improving systems. Each component is pluggable, observable, and recoverable. The agent does not just execute—it learns, remembers, and plans.
This page was rewritten on 17 October 2026. It replaced a templated version that was not specific to its own title. How these pages are written and checked. If anything here is wrong, tell us at [email protected] and we will correct it.
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