Teams are shifting from basic chatbots to autonomous AI agents for real workflows.
Teams shift from basic chatbots to autonomous AI agents for real workflows.
I’ve been part of several recent architecture reviews, and a clear pattern is emerging: the conversation has moved away from simple “prompt‑in, response‑out” loops. Teams that still treat large language models as glorified autocomplete engines risk falling behind as the focus turns to fully autonomous workflows. This shift is not just about refining prompts; it marks a fundamental change in how software accomplishes tasks.
To help engineering leaders grasp these ideas, I’ve been using a concrete example: building an autonomous operations layer for a massive enterprise. The contrast between traditional chatbots and true AI agents is the cornerstone of that explanation.
A standard LLM follows a linear, reactive path. A user supplies input, the model generates output, and the interaction ends. An AI agent, by contrast, pursues objectives rather than merely answering prompts. It can plan, invoke external tools, evaluate results, and iterate until the goal is met. The proactive nature of agents lets them handle complex, multi‑step processes that a simple chatbot cannot manage.
Consider the instruction “optimize our supply chain.” A chatbot would return a list of best practices. An agentic system with tool access could query the ERP, pinpoint a bottleneck in a specific warehouse, pull current shipping rates via an API, and draft a procurement request for approval.
Putting an agentic workflow together requires deliberate engineering. You do not simply release an agent and hope it works; you design a structured, multi‑step process where the LLM performs the heavy cognitive tasks at key points.
A basic, non‑agentic flow looks like this:
user → prompt → LLM → responseAn agentic workflow adds loops, tool calls, and decision gates. For a corporate intelligence dashboard, a typical deployment might be:
trigger → fetch real‑time market data → LLM analyzes volatility → decision logic (is threshold met?) → call trading tool/alert tool → LLM generates executive summary → push to SlackThe GitHub repository useVenice/venice provides extensible, drop‑in data connectors for quickly obtaining customer‑permissioned financial data.
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The autopilot comparison is hilarious. When is the 'full autonomy' update actually coming? I’ve attended quite a few architectural reviews lately, and a clear direction is emerging: the industry is moving away from simple "prompt-in, response-out" loops. If your team still treats LLMs as glorified autocomplete engines, you risk being left behind as the conversation turns to autonomous workflows. This is no longer about improving prompts; it represents a fundamental change in how software executes tasks. To clarify these developments, I’ve been explaining the core concepts to my engineering leads. Rather than relying on academic definitions, consider a high-stakes deployment: building an autonomous operations layer for a massive enterprise. ## The core distinction: Chatbots vs. Agents Many people mistake a standard LLM interface for an actual agent. In production, that distinction is substantial. - Standard LLM: Follows a linear path. You provide input, it produces output, and the process ends. It is reactive. - AI Agent: Works toward objectives rather than simply responding to prompts. It can plan, use external tools, observe their results, and continue iterating until the goal is achieved. It is proactive. If you ask a chatbot to "optimize our supply chain," it will provide a list of recommended practices. Give the same instruction to an agentic system with tool access, and it might query your ERP, identify a bottleneck in a specific warehouse, check current shipping rates through an API, and draft a procurement request for your approval. ## Implementing an Agentic Workflow This is where the real engineering takes place. You do not simply "release" an ag
I'm exhausted by these weekly breakthroughs. Which specific acronym is actually useful and not just hype? The industry is moving away from simple "prompt-in, response-out" loops. If your team still treats LLMs as glorified autocomplete engines, you risk being left behind as the conversation turns to autonomous workflows. This is no longer about improving prompts; it represents a fundamental change in how software executes tasks. To clarify these developments, I’ve been explaining the core concepts to my engineering leads. Rather than relying on academic definitions, consider a high-stakes deployment: building an autonomous operations layer for a massive enterprise. The core distinction: Chatbots vs. Agents Many people mistake a standard LLM interface for an actual agent. In production, that distinction is substantial. Standard LLMs follow a linear path, where you provide input, it produces output, and the process ends; they are reactive. AI agents work toward objectives rather than simply responding to prompts; they can plan, use external tools, observe results, and continue iterating until the goal is achieved; they are proactive. If you ask a chatbot to "optimize our supply chain," it will provide a list of recommended practices. Give the same instruction to an agentic system with tool access, and it might query your ERP, identify a bottleneck in a specific warehouse, check current shipping rates through an API, and draft a procurement request for your approval. Implementing an Agentic Workflow This is where the real engineering takes place. You do not simply "release" an ag. First, you need to define a clear objective, such as optimizing supply chain logistics. Then, select suitable tools like ERP queries and external APIs that the agent can use autonomously. Design the agent's architecture to include a task planner that breaks down objectives into sub-tasks, a tool interface for executing those tasks, and a feedback loop for updating plans based on real-time data.
It's frustrating how vague the documentation is. Instead of treating LLMs as glorified autocomplete engines, try building an autonomous operations layer for a massive enterprise to see how agents proactively plan and iterate rather than just reacting to prompts. Which core mechanic is the biggest hurdle for you right now?
Your cinematic style works brilliantly for high-impact content, but I’ve noticed the same energy can translate surprisingly well to shorter technical guides—especially when you anchor it in a concrete deployment scenario, like comparing a reactive "prompt-in, response-out" LLM to an agentic system that actively queries tools (e.g., an ERP or API) to solve a problem. That shift from theory to a tangible workflow (e.g., "optimize our supply chain") makes the difference between abstract advice and actionable insight. Does that approach resonate for your audience?
Got red rocks instead of a base! Would you actually take 100 forints and a sandwich for it, or just query your ERP, identify a bottleneck in a specific warehouse?
My laptop is screaming while running local agents. Anyone found a way to lower the resource hogging? I'm trying to move toward autonomous workflows where the agent works toward objectives rather than simply responding to prompts, but the hardware strain is real.
Overwhelmed by this pace! Which newsletter actually filters the noise without missing the big weekly breakthroughs? Instead of just treating LLMs as glorified autocomplete engines, try building a high-stakes autonomous operations layer for your enterprise to see how agents proactively handle complex goals.
The jargon is getting out of control—terms like "harness" often overpromise what these tools can actually do in practice. For example, if you’re still framing LLMs as just prompt-in, response-out systems, you’re missing the shift toward autonomous workflows where the model actively plans, executes, and iterates toward goals. Even in simple use cases, start by defining a clear objective (like automating a repetitive task) and then map out how the system will interact with external tools or APIs to achieve it—don’t just treat it as a glorified autocomplete. That’s where the real differentiation begins.