🚀 Part 3: Inside an AI Agent — Tools, Memory, Planning & Execution (DevOps Edition)

By Darji — Founder of AgentFlux

🔍 Introduction: What Actually Powers an AI Agent?

In Part 1 and 2, we explored:

• Why DevOps needs AI agents
• What agents are
• How autonomy differs from automation

Now we go under the hood.

This article breaks down the internal components of an AI Agent designed for DevOps, SRE, and cloud operations — the kind of agent powering AgentFlux.

We’ll explore:

• How agents use tools (kubectl, Terraform, CI APIs…)
• How agents use memory for context
• The planning loop that drives autonomous reasoning
• The execution layer that safely applies fixes
• Real DevOps examples with code

Let’s dive into how autonomous engineering actually works.

🧠 Section 1 — The Four Core Systems Inside Every AI Agent

At a high level, an AI agent consists of:

Tools → Memory → Planning → Execution

This is the “brain” of an operational agent.

🔧 1. Tools: How Agents Interact with Real Systems

Tools are permissions that let the agent take action.

In DevOps, this means giving an agent controlled access to:

🛠️ Infrastructure Tools

• Terraform
• Pulumi
• AWS CLI
• Azure CLI
• GCP APIs

☸️ Kubernetes Tools

• kubectl
• Helm
• ArgoCD

🔁 CI/CD Tools

• GitHub Actions API
• GitLab CI
• Jenkins REST API

📊 Observability Tools

• Prometheus
• Grafana
• ELK / OpenSearch
• Datadog
• New Relic

💬 Communication Tools

• Slack
• Teams
• PagerDuty

If an agent doesn’t have tools, it becomes just a chatbot.

Tools turn intelligence → action.

🧠 2. Memory: What an Agent Remembers

Agents need memory to avoid repeating mistakes and to understand context.

Three DevOps memory types:

1️⃣ Short-Term Memory (active context)

Used for:

• Current logs
• Incident timeline
• Current Kubernetes state
• This deployment’s failures

Example:

{ "pod": "api-7d8f9", "error": "CrashLoopBackOff", "attempts": 3 }

2️⃣ Long-Term Memory

Stored patterns:

• Past incidents
• Common fixes
• Stable configurations
• Previous agent actions

Example:

Pattern recognized: Registry token expires every 12 hours. Suggested action: Preemptive refresh.

3️⃣ Tool Memory

Execution history:

• What the agent changed
• What commands it ran
• What succeeded/failed

This prevents loops and dangerous retries.

🧩 3. Planning: How AI Agents Think Before Acting

Planning is where agents decide:

• What’s happening?
• What’s the safest fix?
• What tools should be used?
• What are the side effects?
• Should a rollback be prepared?
• Should a human be notified?

Agent Planning Loop (Medium-Ready Diagram)

1. Interpret Observations 2. Generate Hypotheses 3. Evaluate Risk 4. Compare Possible Actions 5. Select Best Action 6. Execute or Escalate

🔍 Example: Agent Analyzing Kubernetes Errors

Input logs:

Readiness probe failed after 3s timeout CrashLoopBackOff detected 

Agent planning output:

{ "root_cause": "readiness probe too strict", "proposed_actions": [ "increase timeoutSeconds to 10", "restart deployment", "validate CPU/memory limits" ], "risk_score": 0.12 }

The agent builds a multi-step plan — not just a single action.

⚙️ 4. Execution: How Agents Apply Safe, Controlled Fixes

Execution is where the agent acts, using “tools”.

Safe execution requires:

✔️ Guardrails ✔️ Permission boundaries ✔️ Rollback strategy ✔️ Validations ✔️ Logging everything

Example: Agent Fixing a Terraform Drift

Agent Decision:

State drift detected: Autoscaler set to min=1, expected min=3 Apply terraform fix with plan validation.

Agent Action Code:

import subprocess

def terraform_plan(): return subprocess.run(["terraform", "plan"], capture_output=True)

def terraform_apply(): return subprocess.run(["terraform", "apply", "-auto-approve"])

plan = terraform_plan()

if "autoscaler" in plan.stdout.decode(): terraform_apply()

Execution is ALWAYS followed by:

• Monitoring
• Validation
• Final confirmation

Agents must close the loop.

🧠 Section 2 — How All the Pieces Fit Together

Here is the full architecture diagram of an AI DevOps agent:

┌──────────────────────────┐ │ OBSERVE │ │ Logs, Metrics, Events │ └──────────────┬───────────┘ │ ┌───────────▼───────────┐ │ MEMORY │ │ ST, LT, Tool Memory │ └───────────┬───────────┘ │ ┌───────────▼───────────┐ │ PLANNING │ │ Risk, RCA, Actions │ └───────────┬───────────┘ │ ┌───────────▼───────────┐ │ TOOLS │ │ Terraform, kubectl… │ └───────────┬───────────┘ │ ┌───────────▼───────────┐ │ EXECUTION │ │ Apply fix, rollback │ └────────────────────────┘

This architecture powers the autonomous behavior described in Part 1 & 2.

🧪 Section 3 — Real Scenario: End-to-End Agent Flow

Scenario:

Production API is failing readiness probes.

Full Agent Flow:

Observe:

Readiness probe timeout: 3s → failing CrashLoopBackOff

Analyze:

{ "root_cause": "probe too strict", "confidence": 0.91 }

Decide:

• Patch deployment
• Increase timeout
• Restart pods

Act:

kubectl patch deployment api \ --patch '{"spec":{"template":{"spec":{"containers":[{"name":"api","readinessProbe":{"timeoutSeconds":10}}]}}}}'

Validate:

• Pods stable
• Error logs stopped
• Latency normal

This is autonomous DevOps in action.

🔮 Section 4 — The Future: Multi-Agent DevOps Teams

AgentFlux will evolve into a system where multiple agents collaborate:

Examples:

• Pipeline Agent → detects CI issues
• Kubernetes Agent → heals clusters
• Infra Agent → fixes Terraform drift
• SLO Agent → monitors reliability
• Security Agent → patches vulnerabilities

These agents form a digital DevOps workforce.

🧵 Final Thoughts

AI agents don’t replace DevOps engineers. They replace:

• Manual debugging
• Noisy alerts
• Repetitive fixes
• Human fatigue

Engineers move towards:

✨ Architecture ✨ Reliability engineering ✨ Agent supervision ✨ Strategic decision-making

This is the future we’re building with AgentFlux.

📌 Next Article: Part 4 — Building a Real DevOps Agent (Step-by-Step Tutorial)

🚀 Part 3: Inside an AI Agent — Tools, Memory, Planning & Execution (DevOps Edition) was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.