What Is Agentic Engineering and How Do You Build Autonomous AI Agents?
The difference between a chatbot and an autonomous AI agent, what an agent harness is, which components a real agent must have, and how Claude Code Agents, OpenClaw, and Hermes differ from one another.
A good chatbot knows how to answer. An autonomous AI agent knows how to work.
The difference is not just the model. The difference is everything built around it: context, memory, tools, permissions, an action loop, tests, documentation, and the conditions under which it stops and asks for approval.
This is what I call Agentic Engineering: the engineering of the work environment that enables a model to move from an assistant that talks to a digital worker that executes.
Why Do We Even Need a New Term?
Over the past year, everyone started calling everything an “agent.” A customer service bot? An agent. A chat with GPT? An agent. An automation in Zapier? Also an agent.
But there is a significant difference between a system that answers a question and a system that receives a goal, plans a path, activates tools, checks the result, corrects itself, and sometimes keeps working even after we have closed the window.
If I write to a chatbot: “Write me a thank-you email to a client,” it returns text.
If I write to an agent: “Summarize the meeting, identify action items, update Notion, send a follow-up email, and if anything is missing ask me,” it needs to understand the goal, access tools, decide on an order of operations, and return a result that can be trusted.
That is no longer just Prompt Engineering. That is Agentic Engineering.
What Is Agentic Engineering?
Agentic Engineering is the design of the entire system that allows an agent to actually work.
The model is the brain, but a brain without a body accomplishes nothing. For an agent to act in the world it needs a work environment that connects it to things it can read, modify, run, and verify.
To see this clearly, think of it like onboarding a new employee:
Define identity and goalWho the organization is, what the agent is trying to achieve, and what counts as a good outcome.
Provide tools and permissionsNot every tool in the world — only what the task and risk level actually require.
Set boundariesWhat it may do autonomously, what requires approval, and what the agent must never do.
Build useful memoryWhat needs to persist within a session, what is stored long-term, and what should not be stored at all.
Measure the workLogs, tests, cost, quality, and what happened when something failed.
Define a stopping pointWhen there is risk, uncertainty, or a sensitive external action, a human steps back in.
In agent systems this is sometimes called an agent harness: the harness that holds the model within a safe and productive work track. It is not a single product. It is a collection of components: context, tools, memory, permissions, a task queue, logs, tests, and a runtime environment.
In plain terms: the harness is the difference between “a model that talks” and “an agent that works.”
What Must an Autonomous Agent Have?
In the practical definition used here, an agent is a system in which the model does not simply follow a fixed, predetermined path, but knows how to choose the next step, use tools, read the result, and decide whether to continue, correct course, or stop. This is a direct continuation of the distinction between an Automation Workflow and an Agentic Workflow. When we build an agentic workflow, we are not just building a smart chat interface; we are building a work environment with instructions, tools, handoffs, guardrails, structured outputs, and a runtime that can be observed and traced.
In practice, a solid autonomous agent needs at least the following components:
A clear goalNot just "help me," but a definition of what success looks like.
ContextWho the user is, what the project is, what has already happened, and what must not be broken.
ToolsFiles, browser, calendar, email, terminal, APIs, databases, or any other system.
MemoryWhat happened now, what happened in previous sessions, and what is worth learning for the future.
An action loopThink, act, observe the result, decide on the next step.
Boundaries and permissionsWhat it may do autonomously and what requires approval.
Observability and logsKnowing what the agent did, how much it cost, where it failed, and how to reproduce it.
Stopping and human oversightA good agent also knows how to say "I'm not sure — a human needs to weigh in."
Without these, we do not have an agent. We have a chat interface with a few tools attached.
So What Is the Difference from a Chatbot?
A chatbot lives inside a conversation. It receives text and returns text.
An agent lives inside a work environment. It can open a file, edit it, run a test, check a browser, send a message, come back with an error, and try again.
For example:
Request
Chatbot
Autonomous Agent
”Write me a post”
Returns a draft
Writes, checks tone, adapts to the platform, and saves versions
”Find a bug”
Suggests ideas
Reads code, runs tests, modifies files, and verifies the issue is resolved
”Manage a task for me”
Explains what to do
Opens tasks, tracks status, and sends reminders when needed
”Summarize a meeting”
Summarizes text it was given
Pulls the recording, transcribes, summarizes, sends a follow-up, and updates the calendar
The important point: an agent does not have to be completely free. On the contrary. Good agents are typically autonomous within very clearly defined boundaries.
Types of Autonomous Agents
Single agent with tools
This is the simplest structure: one model, clear instructions, and a limited list of tools. It suits a single, relatively well-defined task where there is no need for a team of specialists.
Real-world example: A code agent that receives a small issue, reads the relevant files, modifies one function, runs a local test, and returns a summary of what changed.
Primary agent and subagents
Here a manager understands the task and distributes work to specialist agents. This is the manager pattern in practice: one agent holds the big picture while subagents — such as subagents — each receive their own context and tools.
Real-world example: Preparing a new article: a research agent gathers sources, a writing agent builds a draft, and a review agent checks accuracy, links, and tone before a human approves.
Agent with long-term memory
This is an agent that does not start every conversation from scratch. It remembers preferences, decisions, mistakes, and recurring patterns. The advantage is personalization; the risk is that you need tight control over what is stored and who has access.
Real-world example: A personal learning agent that remembers which topics you have already covered, where you got stuck, which explanation style works for you, and builds the next practice session from that history.
Agent that runs in the background
This agent does not wait only for a chat message. It can run on a schedule, check things, send alerts, or perform maintenance within defined boundaries.
Real-world example: Every morning the agent checks which articles on the site have lost traffic, compares against Search Console, suggests three SEO actions, and saves a draft task for approval.
Multi-agent system
Here several agents work together: planning, execution, review, testing, and documentation. This is powerful, but also more expensive, slower, and harder to debug.
Real-world example: A software release process: one agent summarizes changes, a second runs tests, a third scans for security risks, and a fourth prepares release notes for human review.
This is why the most important principle is to start simple. Do not build a team of agents if a single model call with one tool solves the problem.
Claude Code Agents are Claude Code's approach to subagents within a development environment. Each subagent is a specialist assistant with a description, a prompt, and a set of permitted tools. This is especially powerful for code, research, review, and planning, because you can activate specialists without mixing all the context into the main conversation.
The advantage: a clear structure, well-suited to development tasks, and naturally connected to files, the terminal, and tools. The limitation: it is not necessarily a 24/7 personal assistant with voice, messaging, cron, and multi-layered memory out of the box.
OpenClaw is a personal AI assistant that runs on your own devices, connects to existing messaging channels, and can act as a local gateway for sessions, tools, and events. Its core capabilities include multi-channel messaging, voice, a live canvas, routing between agents, skills, a background daemon, and a local-first approach.
The advantage: it feels like an always-available personal assistant that reaches you on WhatsApp, Telegram, Slack, Discord, and more. The consideration: the more channels and real tools an agent is connected to, the more critical the security model becomes.
Hermes by Nous Research emphasizes a different idea: an agent that improves over time. It is built around a learning loop, skill creation from experience, skill refinement through use, search across past conversations, cron scheduling, operation via Telegram, Discord, Slack, WhatsApp, Signal, and CLI, and support for multiple execution backends.
The advantage: memory, learning, and self-improvement are first-class citizens, not add-ons. The consideration: a system like this demands strong discipline around what the agent learns, when it modifies a skill, and how you verify that an improvement is actually an improvement.
Interactive Comparison Table
The table below is based on an internal capability review of three approaches: Claude Code Agents, OpenClaw, and Hermes. I left out a Jarvis column to keep the focus on what each existing system actually does.
Open a category to see the capabilities inside it. Each row shows the capability name, a short explanation, and each agent’s status, with no second click required.
Capability Comparison: Claude Code Agents / OpenClaw / Hermes
The ratings reflect a snapshot based on capability reviews and each project's own documentation. "Signature" means the capability is a prominent part of the system's identity, not just another checkbox.
PresentSignaturePartialAbsent
Identity & Context
Context root fileThe agent's main instruction file. It explains the project, rules, style, and boundaries before work begins. In OpenClaw this is part of injected workspace files such as AGENTS.md, and in Hermes it can be .hermes.md, HERMES.md, or AGENTS.md.AgentsPresentOpenClawPresentHermesPresent
User profile / identityStable information about the user or organization: role, preferences, values, customers, work style, and long-term context. OpenClaw uses USER.md and IDENTITY.md for this, while Hermes combines identity, SOUL.md, and user memory.AgentsPartialOpenClawSignatureHermesSignature
Voice / style guideWriting and speaking rules that define tone, preferred language, level of detail, and what the agent should avoid. In OpenClaw and Hermes this is not absent: SOUL.md is where stable voice, style, and agent personality live.AgentsPartialOpenClawSignatureHermesSignature
Auto-load contextThe ability to load context files automatically at the start of work, so every task does not begin from zero. In practice, the agent opens the important documents, reads the rules, and only then starts acting.AgentsPresentOpenClawPresentHermesPresent
Agent Framework
Agent definition formatThe format for creating a new agent: name, description, instructions, allowed tools, boundaries, and activation rules. A fixed format turns an agent from an improvised prompt into a work unit that can be copied, tested, and improved.AgentsSignatureOpenClawPresentHermesPartial
Agent registryThe place where agents are listed and organized, so the system knows which specialists exist and when to use them. Without a registry, the manager does not know whether a researcher, tester, writer, or planner already exists.AgentsPresentOpenClawPresentHermesPresent
Tool permissionsDefines which tools each agent may use. This reduces risk and prevents unnecessary access to sensitive systems, such as allowing a research agent to read files but not delete or publish.AgentsPresentOpenClawPresentHermesPresent
System prompt structureThe instruction structure that guides the agent: role, goal, workflow, safety rules, and expected output format. When the structure is clear, complex tasks become more consistent because the agent knows how to think and how to respond.AgentsPresentOpenClawPresentHermesPresent
Subagent invocationThe mechanism for calling a specialist agent at the right moment, passing context to it, and receiving the result back. For example, a manager agent can ask a research agent to gather sources and then pass the summary to a writing agent.AgentsPresentOpenClawPresentHermesPresent
Primary vs subagent splitSeparation between the agent that holds the big picture and specialist agents that handle research, writing, review, or execution. This keeps responsibility clean: one agent manages the goal while the others execute defined parts.AgentsAbsentOpenClawSignatureHermesPartial
Memory System
Tier 1 - Working memoryThe information currently inside the conversation context window. It is very useful for immediate decisions, but disappears when the session ends or fills up, so it cannot be the only memory layer.AgentsPresentOpenClawPresentHermesPresent
Tier 2 - Session memoryA summary of what happened in a previous session, so the agent can continue from the same point without rereading the full history. It fits multi-day projects where the agent must remember what was tried and what is still open.AgentsAbsentOpenClawPresentHermesPresent
Tier 3 - Long-term identityStable memory about the user or project: preferences, decisions, principles, and work style that should not change every conversation. For example, a rule that the site is always RTL-first and never uses emojis belongs in long-term identity.AgentsAbsentOpenClawPartialHermesSignature
Tier 4 - Episodic eventsA record of events and outcomes over time, such as completed tasks, decisions, or mistakes that should not be repeated. It gives the agent a history of experience, not only a list of general preferences.AgentsAbsentOpenClawPresentHermesPresent
Tier 5 - Procedural memoryMemory of how to perform an action, not only what happened. This is where skills can improve from experience and reduce repeated work, such as a repeatable process for checking an article before publishing.AgentsAbsentOpenClawAbsentHermesSignature
Vector searchThe ability to search memory by meaning, not only exact words, so relevant conversations, decisions, or examples can be found. This lets the agent find guidance about table design even if the exact phrase was never written.AgentsAbsentOpenClawAbsentHermesPresent
Skills & Tools
Skills locationThe location where skill files are stored. A clear location lets the agent discover existing abilities and use them consistently instead of inventing a new process every time.AgentsPresentOpenClawPresentHermesPartial
Skills formatA fixed structure for a skill: when to use it, which files or tools it requires, and what workflow is expected. When a skill is written well, it works like an operating procedure the agent can reuse.AgentsPresentOpenClawPresentHermesPartial
Skills auto-creationThe ability to create a new skill from a repeated action or successful experience, so the system learns processes and not only memories. This turns a one-time fix into a reusable habit for the system.AgentsAbsentOpenClawAbsentHermesSignature
Shell commandsAccess to run commands on the computer. It is powerful for testing, building, and running work, but needs clear boundaries and permissions so the agent does not take risky actions without control.AgentsPresentOpenClawPresentHermesPresent
File read/writeThe ability to read, create, and edit files. Without this, the agent is only an adviser; with it, it can work directly on a project, fix code, update content, and save real output.AgentsPresentOpenClawPresentHermesPresent
Browser controlThe ability to open sites, click, read results, and test interfaces. Useful for research, UI checks, and live information gathering, such as verifying that an article still looks good on mobile after a change.AgentsPartialOpenClawPresentHermesPresent
Email integrationEmail access lets the agent read context, draft replies, send messages, or prepare human-approved drafts. The permission line matters: not every agent should send email, sometimes a draft is enough.AgentsPartialOpenClawPresentHermesPresent
Calendar integrationCalendar access lets the agent detect availability, schedule meetings, follow events, and connect work to real time. For example, a personal agent can suggest an open slot and prepare a reminder before the meeting.AgentsPartialOpenClawPresentHermesPresent
Communication & Channels
Terminal / CLIA command-line work interface. Good for technical agents that run tools, read files, and return results to a developer, but less natural for users who need a visual or mobile experience.AgentsPresentOpenClawPresentHermesPresent
Visual canvas / artifactA live visual workspace where outputs, files, or work state can be seen in real time instead of receiving only a text answer. This matters when an agent builds, designs, or edits something that needs to be inspected visually.AgentsAbsentOpenClawSignatureHermesAbsent
Multi-channel messagingThe ability to meet the agent in the channels where people already work: WhatsApp, Slack, Discord, Telegram, and more. It makes the agent part of daily work instead of a separate tool people must remember to open.AgentsAbsentOpenClawSignatureHermesPresent
Voice input (STT)Speech-to-text so a user can talk to the agent, send voice instructions, or activate it from mobile. It fits personal assistants, work on the move, or moments when long typing is inconvenient.AgentsAbsentOpenClawSignatureHermesAbsent
Voice output (TTS)Turning the agent response into speech. Useful for personal assistants, summaries on the move, or interaction without a screen, especially when the user needs a short answer without stopping to read.AgentsAbsentOpenClawSignatureHermesAbsent
Mobile interfaceA usable phone interface, so the agent is available outside the development environment or desktop. In practice, this means lightweight screens, short actions, and support for messages, voice, and notifications.AgentsAbsentOpenClawPresentHermesAbsent
Orchestration
Manager patternA pattern where a main agent understands the goal, breaks it into tasks, activates specialists, and combines their results. It behaves like a small project manager that decides who should do what and checks that the final output fits together.AgentsPresentOpenClawPresentHermesPresent
ReAct loopA loop of reasoning, action, and observation. The agent plans a step, uses a tool, reads the result, and decides what to do next, so it progresses through feedback instead of only answering once.AgentsPresentOpenClawSignatureHermesSignature
Heartbeat (background)Background activity that happens even without a new message, such as periodic checks, monitoring, reminders, or continuing queued work. This is one of the clearest differences between a chatbot that waits and an agent that keeps operating.AgentsAbsentOpenClawSignatureHermesPresent
Schedule / CronRunning tasks on a fixed schedule, such as a daily check, weekly report, or action that must happen at a specific time. For example, an agent can open the data every morning, detect anomalies, and prepare a short report.AgentsAbsentOpenClawPresentHermesPresent
Inbox / queueA task queue that collects requests, events, or alerts the agent should process by order, priority, or working rules. It prevents events from getting lost when they arrive faster than the agent can handle them.AgentsAbsentOpenClawPresentHermesPresent
Self-Improvement & Observability
LoggingA record of actions, decisions, errors, and results. Without logs, it is hard to understand why the agent did something, where it failed, and what needs to change to improve behavior.AgentsPartialOpenClawPresentHermesPresent
Self-improving skillsThe ability to improve skills or process from accumulated experience. Powerful, but it needs review so mistakes are not preserved or a one-time experiment does not become a permanent rule without testing.AgentsAbsentOpenClawAbsentHermesSignature
Failed attempts learningLearning from attempts that did not work: what broke, why it happened, and how to avoid the same path next time. A real agent should remember not only success, but also the boundaries around approaches that already failed.AgentsAbsentOpenClawPartialHermesPresent
Token trackingTracking token usage to understand cost, context load, and efficiency for each task or agent. Without it, it is hard to know whether an agent is working well or simply spending too much context and money.AgentsPartialOpenClawPresentHermesPresent
Cost monitoringFinancial tracking for runs, models, and external tools, so you know when the agent becomes too expensive or inefficient. It helps route simple tasks to cheaper models and reserve stronger models for work that actually needs them.AgentsPartialOpenClawPresentHermesPresent
Handoffs (peer-to-peer)Transferring responsibility between agents. One agent finishes part of the work and passes organized context to another, so the next agent does not have to guess what happened and what remains open.AgentsPartialOpenClawAbsentHermesPartial
Multi-model routingChoosing a model by task type: a fast cheap model, a strong model for complex reasoning, or a local model for sensitive work. This prevents the system from treating every action as if it needs the same cost, speed, and privacy level.AgentsAbsentOpenClawPresentHermesPresent
Local model (Ollama)Running a local model, usually through Ollama, to keep sensitive information on the computer or reduce cost. It is not always the strongest option, but it gives better control when privacy or local availability matter.AgentsAbsentOpenClawPresentHermesPresent
What We Learn From the Comparison
There is no single winner here. Each system emphasizes a different idea.
Claude Code Agents are strong when we want to work inside a software project, define specialists, read files, run checks, and keep the agent close to the code.
OpenClaw is strong when we want a live personal assistant that exists inside messaging channels, can work in the background, shows a canvas, and feels less like a developer tool and more like an operating layer for a personal assistant.
Hermes is strong when we want an agent that accumulates experience: memory, search across the past, skills that are created and improved, and an execution environment that does not have to be our own computer.
The important lesson is not “which tool should I choose?”. It is what kind of autonomy we actually need.
How Do You Start Building an Agent Without Overcomplicating It?
The common mistake is to start with “I want a 24/7 agent with memory, voice, and WhatsApp”. That is the fastest path to something impressive that nobody trusts.
A better path:
Start with one task that repeats and has clear value.
Define success so you know whether the agent actually helped or only looked impressive.
Give it a few tools one or two tools, not a whole system on day one.
Run in a limited environment without free access to delete, publish, or send things externally.
Keep logs so you can understand decisions, cost, errors, and repeated attempts.
Add memory carefully only after you understand what truly needs to be stored.
Increase autonomy gradually only after the system proves it is stable and trustworthy.
For example, instead of “an agent that manages my website”, start with “an agent that identifies old articles with traffic decline and suggests three improvement actions”. Only after that works should you add draft creation, link checking, or scheduling.
The Principle: Autonomy With Accountability
A good agent is not an agent that does everything alone. A good agent knows when to work by itself and when to stop.
For low-risk tasks, it can execute. For example: search, summarize, draft, or run a local check.
For medium-risk tasks, it should propose and ask for approval. For example: send an email, publish content, or change an important file.
For high-risk tasks, it should stay in recommendation mode. For example: deletion, payment, access to sensitive information, or permission changes.
That is why Agentic Engineering is not only a technical question. It is a question of trust, boundaries, process, and control.
Bottom Line
The move from chatbot to agent is not a move from “nice text” to “nicer text”. It is a move from a conversation interface to an action system.
The model matters, but it is not enough. What determines whether the agent is useful is the harness around it: context, tools, memory, permissions, logs, and the ability to stop at the right time.
When this is built well, an AI agent does not replace the human. It replaces the noise around the work and leaves the human with the decisions that truly require judgment.
