Agentic AI FAQ: Your Complete Guide to Autonomous Agents
I've spent the last six months watching engineering teams experiment with autonomous AI agents. Some teams are shipping features faster than ever. Others wasted weeks on agents that hallucinated APIs and broke prod.
The difference? Context.
An AI agent without codebase context is like a new hire who can't access your wiki, can't ask questions, and has never seen your code. They're smart, but useless.
Let's talk about what actually works.
What is agentic AI?
Agentic AI refers to AI systems that can take a goal, break it down into steps, execute those steps, and adapt based on results — without constant human supervision.
Traditional AI: You ask a question, get an answer.
Agentic AI: You give a goal ("Add rate limiting to our API"), and the agent figures out how. It reads your code, identifies where to make changes, writes tests, and creates a PR.
Think of it as the difference between a calculator and an intern. The calculator needs exact instructions. The intern can be told "make this faster" and figure out the details.
The key word is "autonomy." Agents make decisions. They call functions, query databases, run commands, and iterate when things fail. This is why they're powerful. Also why they're dangerous.
How do autonomous agents differ from chatbots?
Chatbots respond. Agents act.
ChatGPT is a chatbot. You type something, it responds. The conversation is the product.
An autonomous agent has tools. It can execute code, make API calls, read files, write to databases. It operates in a loop: observe, think, act, repeat.
Example: You tell a chatbot "the login is broken." It might suggest checking authentication logic. You tell an agent "the login is broken," and it:
Reads your authentication code
Checks recent commits
Looks at error logs
Identifies the bug
Proposes a fix
Runs tests
Opens a PR
The chatbot gives advice. The agent ships code.
This distinction matters because the failure modes are different. A chatbot can waste your time with bad advice. An agent can waste your time and break your production environment.
What can AI agents actually do today?
Let's be specific. Here's what works right now:
Code generation for well-defined tasks. "Add a new API endpoint for user preferences" — this works. The agent can scaffold routes, add database migrations, write tests. Success rate is high when the task is similar to existing patterns in your codebase.
Bug investigation and triage. Agents are genuinely good at reading stack traces, finding relevant code, and narrowing down root causes. They won't always fix the bug correctly, but they'll find it.
Documentation generation. This is underrated. An agent can read your functions, understand what they do, and write decent docstrings. Not poetry, but better than nothing.
Refactoring. Renaming variables, extracting functions, updating import statements across files — mechanical work that requires understanding context but not creativity.
Test writing. Given a function, agents write reasonable unit tests. They won't catch edge cases you haven't thought of, but they'll cover the happy path and obvious errors.
What doesn't work: Complex architectural decisions. Debugging distributed systems. Anything requiring deep domain knowledge. Security audits (please don't).
The pattern is clear. Agents are excellent junior engineers. They execute well-defined tasks. They struggle with ambiguity and novel problems.
How do agents access codebase information?
This is where most teams fail.
You can't just point an agent at your GitHub repo and expect magic. LLMs have context windows measured in tokens, not gigabytes. A medium-sized codebase has millions of lines. You need to be selective about what the agent sees.
Three approaches:
RAG (Retrieval Augmented Generation). Convert code to embeddings, store in a vector database, retrieve relevant chunks based on the query. This works for finding similar code but struggles with understanding relationships. It might retrieve the function you're interested in but miss the config file that controls its behavior.
Manual context curation. You explicitly tell the agent "here are the five files you need." This works for small tasks but doesn't scale. It also requires you to know what the agent needs, which defeats the purpose of autonomy.
Structured codebase indexing. This is what actually works. You need a system that understands your codebase structure — what imports what, which functions call which, how data flows through your system. At Glue, we build exactly this: a knowledge graph of your code that agents can query. Instead of "find me text similar to this," agents ask "what functions modify user state?" and get correct answers.
The difference is semantic understanding vs. text similarity. One works, one doesn't.
What are the biggest challenges with agentic AI?
Context is the killer problem. I mentioned this already, but it's worth repeating. Every AI disaster I've seen started with insufficient context. The agent didn't know about that critical validation function. It missed the environment-specific configuration. It "fixed" code that was intentionally written that way.
Agents lie confidently. LLMs hallucinate. When they're uncertain, they don't say "I don't know" — they make something up. An agent might reference functions that don't exist, import libraries you don't use, or confidently implement a pattern that's explicitly banned in your style guide.
The cost of mistakes is high. A bad code suggestion wastes five minutes of human review. A bad autonomous action can break prod, expose security holes, or delete data. You need guardrails, and guardrails reduce autonomy.
Agents are slow. Running an agent costs 10-100x more in API calls than a single LLM query. Each step requires thinking time. What takes you 30 seconds might take an agent five minutes. This matters for iteration speed.
They're hard to debug. When an agent fails, you need to trace through its reasoning. Why did it choose that approach? What context informed its decision? Most agent frameworks give you a wall of text logs. Good luck.
How do I evaluate if an agent solved a task correctly?
You can't fully automate this yet.
For code generation, run your test suite. If tests pass, the agent probably didn't break anything. "Probably" is doing a lot of work in that sentence.
For refactoring, use static analysis. Check if the code still compiles, if type checking passes, if linting is clean.
For documentation, manual review is the only option. Auto-generated docs range from "genuinely helpful" to "technically accurate but useless" to "completely wrong."
The real answer: Treat agents like junior engineers. Review everything. The value isn't that you can skip review — it's that you can skip writing the first draft.
One team I talked to uses this process:
Agent generates code
Automated checks (tests, linting, security scans)
Human review focused on logic and design
Agent makes revisions based on feedback
Repeat until merge
This works because it plays to each participant's strengths. Agents write boilerplate fast. Humans catch subtle bugs and architectural issues.
What tools do I need to build AI agents?
The ecosystem is moving fast. Here's what matters today:
An LLM API. OpenAI, Anthropic, or local models via Ollama. Claude is particularly good at following instructions and admitting uncertainty. GPT-4 is faster. Pick based on your use case.
An agent framework. LangChain and LlamaIndex are the big ones. CrewAI if you want multi-agent systems. AutoGPT if you like living dangerously. Honestly, for production use, I'd build something custom. The frameworks are heavyweight and assume use cases you don't have.
A code understanding system. This is non-negotiable. Your agent needs to query codebase structure, find definitions, trace dependencies. Glue provides this as an API — you can plug it into any agent framework via MCP (Model Context Protocol). Alternative: build your own indexing pipeline and spend six months getting it right.
Observability. You need to see what your agent is doing. Log every action, every context fetch, every LLM call. When things go wrong (they will), you need to debug.
Sandboxing. Run agents in isolated environments. Use containerization. Limit filesystem access. Don't give production credentials to an autonomous system that hallucinates.
Should I use MCP for agent-to-codebase integration?
Yes. Model Context Protocol is the closest thing we have to a standard for connecting LLMs to external systems.
Instead of every agent framework implementing its own way to query your codebase, MCP defines a protocol. Your codebase exposes capabilities as MCP servers. Agents (Claude, Cursor, your custom stuff) consume those capabilities.
Glue implements MCP, which means you can connect our codebase intelligence to any MCP-compatible agent. The agent can ask "what are all the authentication flows?" or "show me files with high churn and complexity" without you writing custom integration code.
The alternative is building point-to-point connections between every agent and every data source. That doesn't scale.
MCP isn't perfect — it's still early — but it's the right architecture. Adopt it now before you build custom integrations you'll need to migrate later.
What's next for autonomous agents?
My predictions:
Specialized agents beat general-purpose ones. Instead of one agent that does everything, you'll have agents specialized for testing, for documentation, for performance optimization. They'll have different models, different tools, different guardrails.
Agents will run continuously, not on-demand. Think of them as always-on teammates. They monitor your codebase, flag issues, propose improvements. You review and approve rather than explicitly invoking them.
Context quality becomes the moat. As models commoditize, the competitive advantage is data. Teams with better codebase understanding will build better agents. This is why we're betting on structured code intelligence at Glue.
Humans stay in the loop. Fully autonomous agents that ship to prod without review? Not happening soon. The reliability isn't there. What changes is the ratio — one human might oversee ten agents.
Agent collaboration gets weird. Multiple agents working together, each with different specialties, negotiating approaches and reviewing each other's work. This sounds like sci-fi but teams are experimenting with it today.
The future isn't "AI replaces engineers." It's "engineers who effectively direct AI agents replace engineers who don't."
The bottom line
Autonomous agents work when they have three things: clear goals, appropriate tools, and deep codebase context.
Most teams nail the first two and completely miss the third.
You can build impressive demos without good context. You can't build production systems.
If you're experimenting with agents, invest in code understanding first. Index your codebase properly. Build a knowledge graph. Make it queryable. Then point your agents at it.
Or use something that already does this. That's literally what we built Glue for.
Either way, the teams that win with agentic AI won't be the ones with the fanciest models or the most sophisticated agent frameworks. They'll be the ones that gave their agents the context they needed to not screw up.