From demos to dependable agents: A practical path
Download our new e-book to learn how to take agents from prototype to production
Created on September 5|Last edited on September 5
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If you’ve been dazzled by agentic AI prototypes but hesitated to ship, this e-book is for you. The main insight: agents can materially boost productivity and decision-making if you build them with the right workflow that ensures quality, governance, security, and cost. In particular, evaluation can’t be an afterthought, but should be baked into both development and production workflows so you can iterate at scale to make agents reliable and safe for real-world usage.
What counts as an agent—and why that matters
Think of an agent as a reasoning model inside a system that plans actions, uses tools, remembers context, and keeps iterating until it meets a goal. It’s not just a prompt, it’s a loop with planning, tool calls, memory, and verification—all coordinated by a harness around the model.
Where teams are finding traction today
Adoption clusters around three use cases:
- Coding agents that write and run code: high value and straightforward to verify.
- Research agents that gather and synthesize information (think Perplexity-style), where search-scoring can help validate results.
- Customer agents that now transact and fetch internal data by coordinating with other agents.
These lead in production today; an agent-everywhere future is next. What’s delaying takeoff?
Why so many pilots stall
Agents aren’t deterministic. Tiny tweaks to models, prompts, or tools can flip outcomes. The way forward is disciplined experimentation plus observability—evaluation, tracing, monitoring—to measure trade-offs and stop regressions. This tooling unlocks reliable behavior and safer, wider use.
The workflow that de-risks your build
The e-book proposes an end-to-end loop you can reuse across projects: explore models and prompts, prototype quickly with trace capture, iterate with objective scorers, deploy with live monitoring and feedback, fine-tune from real traces, and enforce guardrails with verification. It’s a continuous improvement cycle designed to overcome agent uncertainty.
A look at the example build
To make the workflow concrete, the e-book walks through building a financial research agent. A planner creates 5–15 targeted queries; search agents fetch data; a writer synthesizes results with support from fundamentals and risk analysts; and a verification agent audits sourcing and consistency before the final report appears in the dashboard. It’s a small system that demonstrates planning, tool use, memory, and verification working together.
Move from reading to building
If you’re ready to try this approach, the e-book links to a free agents course (co-created with OpenAI experts), a webinar on building with MCP and other protocols, a demo video, and a “hello world” that starts W&B Weave in just a few lines. There’s also guidance for kicking off a proof-of-concept and engaging advisory help if you need it.
Download the e-book for the diagrams, the detailed workflow, and the full example. It’s a field guide for turning agentic ideas into reliable, governed systems without getting stuck in prototype purgatory.
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