Agent vs. Agent: Agentic AI for Additive Manufacturing Build Optimization

Discover how agentic AI optimizes additive manufacturing builds for cost, yield, and throughput in this live Synera webinar with demo and Q&A.

Details

See what agentic AI can do in additive manufacturing (AM)

Additive manufacturing (AM) teams constantly balance material choice, machine selection, and cost. While workflow automation tools support predefined steps, what happens when trade-offs have to be evaluated to determine the optimal strategy? Or when a parameter needs to change without restarting the whole process?

In this 35-minute session, Synera experts compare several agentic AI approaches to solve a real AM engineering challenge: optimizing an AM job to maximize value of a build – highest yield and throughput at the lowest total cost - while staying within a fixed budget.

Through a live demo and Q&A, you'll see how engineering workflows evolve with AI agents that autonomously evaluate constraints, negotiate trade-offs, and complete processes where conventional automation systems stall.

Key Takeaways

See the difference between workflow automation and agentic AI
See a real AM process run by Synera AI agents specialized for engineering
Learn pros and cons of different agent setups
Ask Synera experts about AI in engineering environments

Who is the webinar for?

Additive Manufacturing Team Lead: Leads AM engineers responsible for build preparation, orientation strategy, support generation, and machine allocation under cost and production constraints. ‍
Cost Engineering Team Lead and RFQ Owners: Oversees cost estimation and quotation workflows, balancing material, machine, and process parameters to deliver competitive and accurate pricing. ‍
Manufacturing Engineering Team Lead: Responsible for scaling manufacturing preparation processes and improving automation in complex, constraint-driven production environments.

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Key terms

Agentic AI is AI technology that uses large language models (LLMs) to complete tasks using pre-defined skills and instructions, rather than only generating text or suggestions. In engineering, agentic AI means agents that evaluate, interpret, and act: deciding which workflows to run and adapting their next step without being told exactly how at every turn.
Workflow automation digitizes a fixed engineering process: the steps, logic, and domain knowledge required to complete a task, so it runs the same way every time without manual work. On its own, a workflow follows one predefined path; pairing it with AI (an AI-enhanced workflow) lets specific steps call an LLM for added flexibility, as long as that step's output stays structured enough to feed the rest of the workflow.
AM build optimization is the process of choosing the best combination of machine, part orientation, nesting layout, and support strategy for a 3D-printed part, balancing production cost against delivery constraints. In this webinar's demo, that meant comparing support strategies (line vs. block supports) across available printers to find the option that hit a target cost and lead time.

Comparison table: agentic AI approaches compared in the webinar

Approach	Autonomy	Cost to build & run	Latency	Best suited for
Rule-based workflow	None: every path is predefined; branches and edge cases must be hand-coded as explicit rules.	Lowest to run, but cost of building rises fast with scope. The webinar's own worked example showed a simple linear process becoming far more complex once real branching (machine availability, orientation constraints) was added.	Fastest: steps execute directly along a fixed path, no LLM calls.	Stable, well-understood processes with one predefined "happy path" and few judgment calls.
AI-enhanced workflow	Low: the workflow stays fixed, but specific steps can call an LLM for interpretation; the output has to stay structured enough to feed the rest of the workflow.	Moderate: cheaper to extend than adding more hand-coded rules when only a few steps need judgment.	Slightly higher than rule-based, only at the steps that call an LLM.	Otherwise-stable processes with one or two steps that benefit from added interpretation, without needing full autonomy.
Single-agent system	High: one agent holds every tool (orientation, nesting, support, costing) and one prompt, deciding for itself what to run next.	Simple to set up initially; the trade-off is a longer, less-separated prompt as scope grows.	A slight edge for straightforward requests: one agent calls its own tools directly, with no agent-to-agent handoffs.	Well-scoped processes with a manageable toolset and shorter sessions. The webinar's demo showed it can get "confused" as sessions grow longer or more interactive.
Team of agents (multi-agent system)	High and distributed: a supervisor agent interfaces with the user and routes tasks to domain-specialized agents (orientation, support, nesting, costing), each with only its own tools and prompt.	Each agent's prompt is simpler to write, but the supervisor's task-distribution logic needs its own design and testing. Across both agentic architectures, the webinar put roughly 80% of build effort into the underlying tool workflows and 20% into agent-system setup and testing.	Slightly higher than single-agent on simple requests, due to handoffs between agents, though the speakers noted agents don't sit idle the way human handoffs do.	Scales better as complexity rises: more tools, longer or more interactive sessions, more dynamic decision points. Agents are also more modular; the webinar cited reusing a costing agent in a different multi-agent system.