AI Agents That Actually Do Work

What agents actually are (and aren't)

An AI agent is software that can take actions on your behalf - not just answer questions. The difference matters. ChatGPT can tell you how to update a spreadsheet. An agent actually updates it.

Most "AI agents" you see marketed are really just chatbots with API access bolted on. They fail the moment something unexpected happens. Real agents handle edge cases, recover from errors, and know when to ask for help.

• •Chatbots: Answer questions, generate text, require human follow-up for every actual task.
• •Agents: Complete tasks, use tools, make decisions, handle the complex middle of real work.

Tasks that work well with agents

Not everything should be an agent. The best candidates share a few traits: they're repetitive, they follow patterns (even loose ones), and success is measurable.

• •Research and synthesis: Gathering info from multiple sources, summarizing findings, identifying patterns. The kind of work where someone says "can you look into X and get back to me?"
• •Data processing: Transforming data between formats, cleaning and validating records, enriching datasets from external sources.
• •Monitoring and alerting: Watching systems, docs, or feeds for changes that matter. Knowing when to escalate vs. when to handle it.
• •Outreach and follow-up: Personalized emails, scheduled check-ins, lead qualification - anywhere you're doing "mail merge with intelligence."
• •Admin and operations: Scheduling, form processing, report generation, the stuff that keeps falling through the cracks.

When agents fail (and how to avoid it)

We've seen plenty of agent projects fail. Usually it's one of these:

1. No clear success criteria. If you can't define what "done" looks like, neither can the agent. "Make this better" isn't a task specification.
2. Expecting human-level judgment. Agents are great at pattern-following at scale. They're bad at novel situations that require real expertise or political awareness.
3. Underestimating the "last 20%." Getting an agent to work 80% of the time is easy. Getting it reliable enough to trust without supervision takes 10x the effort.
4. No escalation path. Agents that can't say "I don't know" or "this needs a human" will confidently do the wrong thing.

What it actually takes to build reliable agents

The agent itself is maybe 30% of the work. The rest is infrastructure that makes it reliable:

• •Tool integration: The agent needs to actually do things - read from databases, call APIs, update records. Every integration is a potential failure point.
• •Memory and context: Most LLMs forget everything between requests. Production agents need persistent memory, conversation history, and awareness of past actions.
• •Output validation: You can't just trust the output. Structured responses, schema validation, sanity checks - the agent needs guardrails.
• •Error handling: APIs fail, data is malformed, rate limits hit. Robust agents retry, fall back, and degrade gracefully.
• •Observability: When something goes wrong (it will), you need logs, traces, and the ability to replay what happened.

Is this right for you?

Quick gut check before going further:

• •Do you have tasks that take your team hours but follow predictable patterns?
• •Can you clearly define what "success" looks like for those tasks?
• •Are you willing to invest in documentation and process definition upfront?
• •Do you have someone technical who can maintain and iterate on the system?

If you answered yes to all of those, agents are probably worth exploring. If you're hoping AI will "figure out" an undefined process - that's a different conversation.