What is the difference between an AI agent and a chatbot?

A chatbot responds to messages. An AI agent takes a goal and completes multiple steps to achieve it, including taking actions like running code, browsing the web, or modifying files, without requiring you to direct each step.

How much supervision do AI agents need?

It depends on the task and the stakes. A coding agent running on test data needs less oversight than one deploying to production. Start with more oversight than you think is necessary, then reduce it as you develop confidence in the agent's output for each task type.

Can I use multiple agents on the same project?

Yes. Different agents are good at different tasks. The challenge is keeping a shared record of decisions, progress, and current task state so each agent starts with the right context. A shared record like fenbs handles that.

What happens if an agent makes a mistake?

Recover the affected state and update the task instructions to prevent the same mistake. For irreversible actions, add an explicit review checkpoint before that step in future runs.

Is fenbs itself an AI agent?

No. fenbs is a project and task management tool that AI agents connect to over MCP. It keeps the record of your work, decisions, and progress. The agents you connect to it take the actions.

AI Agents for Productivity: Choose Tasks and Keep Progress

AI agents can handle research, code, writing, and web tasks without you watching every step. The practical challenge is choosing the right agent for each task, knowing how much review it needs, and keeping progress across sessions. Here is how to approach it.

July 5, 2026Updated October 21, 20265 min read

AI agents for productivity are tools that can take a goal and complete multi-step work without requiring you to direct every action. They can run a web search and summarize findings, write and test code, draft a report from raw data, or fill out forms on your behalf. The difference from a chatbot is that they act, not just respond.

The challenge is not finding an agent; it is choosing the right one for the task, knowing how much human review it needs, and keeping the work coherent across sessions. A tool like fenbs keeps shared task context and progress notes outside any one AI app, so any connected agent starts each session knowing where the work stands.

How to decide which tasks suit an agent

Agents do well on tasks that are well-defined, repeatable, and have clear outputs you can check. The best candidates share three characteristics: the steps are describable in advance, the output is verifiable without deep judgment, and a mistake is recoverable.

Well-defined: you can write down the starting inputs, the steps, and what done looks like.
Verifiable: you can check the result in a few minutes without having to redo the work yourself.
Recoverable: if the agent takes a wrong step, fixing it is straightforward.

Tasks that depend on relationships, political context, or subtle judgment do not fit well. Sending a message to a difficult client, making a strategic decision, or evaluating someone's performance are not agent tasks.

A comparison of productivity agents

Productivity agents by task type and review needs
Agent	Best task types	Review needed	Where it works
Claude Code	Software development: write, edit, test, and debug code; automate terminal tasks; manage files.	Review each code change before committing; check tests pass.	Terminal in any development environment.
ChatGPT Work	Web research, online form completion, booking, and multi-step browser tasks.	Review the plan before confirming; verify the result after each task.	ChatGPT interface; tasks run on OpenAI servers.
Devin Desktop	Software engineering tasks including full codebase changes and PR creation.	Review each PR or changeset; approve before merging.	Desktop app, connected to your code repository.
Gemini CLI	Terminal-based tasks, file operations, code assistance, and Google Workspace integration.	Review changed files; verify outputs against expected results.	Terminal; integrates with Google tools.
fenbs	Keeping shared task context, decisions, rules, and progress across all agents and sessions.	People decide; assistants record. Check Where we left off before each session.	Web app, phone app, MCP connection for all listed agents.

Claude Code: coding and terminal tasks

Claude Code is Anthropic's coding agent. It runs in your terminal with access to your file system, codebase, and shell. You give it a task in plain language, and it writes code, edits files, runs tests, and reports back. It is the most capable option for software development work.

Claude Code reads AGENTS.md natively, so you can put project instructions in that file and the agent reads them before starting. It also works well with fenbs over MCP: you can connect it to your project board and have it read the current tasks, decisions, and progress notes at the start of every session.

ChatGPT Work: online tasks

ChatGPT Work handles agentic tasks in a managed browser environment: searching, filling forms, interacting with web apps, and compiling research. It runs on OpenAI's servers, so it continues after you close your browser. You review a proposed plan before the task starts, then check the result when it finishes.

ChatGPT Work does not access your local files or desktop applications. For tasks that stay entirely online, it is practical for research, booking, and web-based data gathering.

How fenbs helps

Give every agent the same starting context

Connect Claude Code, ChatGPT, Devin Desktop, or Gemini CLI to fenbs over MCP. Each agent reads the same project context at session start: the rules, the current tasks, Where we left off, and the glossary. Before it stops, it saves progress so the next session or the next agent picks up from an accurate record.

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Free for one person · No credit card · Works with Claude, ChatGPT, Cursor and more

Devin Desktop: software engineering

Devin Desktop (formerly Windsurf) is a desktop agent for software engineering tasks. It connects to your code repository and can make changes across the codebase, write tests, and create pull requests. The review step is the PR or changeset: you approve before anything merges.

Gemini CLI: terminal and Google Workspace

Gemini CLI is Google's terminal-based agent. It handles file operations, code assistance, and integrates with Google Workspace tools. If your work lives in Google Docs, Sheets, or Gmail, Gemini CLI can be a natural fit for tasks that involve those files.

The review step: what you must not skip

Every agent produces output that needs a human to verify. A well-written task description reduces errors but does not eliminate them. Set aside time after an agent run to check that the output matches the goal, any files modified are in the expected state, and no unintended changes were made.

For irreversible actions, such as sending an email, submitting a form, or deploying code, make the review step explicit in the task: "Show me the email before sending." "List the files you changed before you commit." That checkpoint is where errors are caught cheapest.

Keeping progress across sessions

The biggest productivity loss in agentic work is re-explaining context at the start of every session. An agent that does not know what was decided last time, what is still in progress, and what comes next spends the first part of every session rediscovering the work.

Store that context outside the chat. In fenbs, Where we left off records what was completed, what changed, and the next step. At the start of the next session, the agent reads it and gets to work without the re-explanation.

How to do this with fenbs

Create a free account and add your project and its current tasks.
Write Decisions and rules for the constraints: things agents should always ask about, off-limits actions, approval checkpoints.
Connect the agents you use from Integrations. Each one reads the shared project context at session start.
After each agent run, review the output and update the task status in fenbs.
Ask the agent to save progress and the next step in Where we left off before the session ends.

For more on specific agent types, see types of AI agents and AI agents for beginners.