Stop tweaking prompts.
Start optimizing them.
Build prompts, run experiments across models, auto-optimize with six algorithms, and simulate real conversations - all with evaluation scores at every step. No more guessing.
Thank you for reaching out! I'd be happy to help you build a multi-step agent workflow.
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Based on your requirements, I've configured a 3-node pipeline: the Agent Node handles orchestration, LLM Prompt Node 1 processes the initial analysis, and LLM Prompt Node 2 generates the final response...
Engineering rigor for
prompt development
Create prompts from scratch, generate them from natural language, or start from templates. Every execution shows median latency, token counts, cost, and linked traces - so you know exactly how each change performs before it ships.
Open the workbenchCompare prompt versions, models, and parameter configs against the same dataset in one view. Automated evaluators score every response on accuracy, fluency, coherence, and your custom metrics - no more eyeballing outputs.
Run an experimentStop tweaking prompts by hand. Run ProTeGi, PromptWizard, GEPA, Meta-Prompt, or Bayesian Search to automatically generate, score, and refine prompt variants. Each algorithm targets different failure patterns - from targeted error fixing to full evolutionary optimization.
See the optimizersDefine agent personas and test scenarios - graph-based flows, scripted sequences, or dataset-driven cases. Run voice and text simulations against your agent, then evaluate every turn with automated scoring. Find failures before your users do.
Set up simulations From first draft to
production-grade prompt
Auto-optimize underperforming prompts
Paste a prompt that's not hitting your accuracy bar. Pick an optimizer - GEPA for production-grade, PromptWizard for creative tasks - and let it generate, score, and refine variants automatically.
Compare models on your data
Run the same prompt across GPT-4o, Claude, Gemini, and open-source models. See accuracy, latency, and cost side-by-side - pick the best model for each task, backed by data.
Simulate customer conversations
Define personas and scenarios - then run voice or text simulations against your agent. Score every turn automatically. Ship agents that handle edge cases, not just the happy path.
Debug failures from production
Pull failed traces directly into the workbench. Replay the conversation, tweak the prompt, re-run against the same input - fix issues in context instead of guessing.
Let non-ML teams iterate on prompts
Product managers and domain experts can create prompts from natural language, use guided optimization wizards, and see evaluation scores - no Python required.
Version and promote to production
Every prompt change creates a version. Run evaluations against it, promote through dev → staging → production, and roll back instantly if quality drops.
From idea to production
in three steps
Write or generate your prompt
Create prompts from scratch, describe what you want in natural language and let AI generate one, or start from team templates. Configure model, parameters, and tools - all in one view.
Experiment and optimize
Run the prompt against your dataset. Compare model and config variants side-by-side with automated evaluation scores. If results aren't good enough, run an optimizer to automatically find better variants.
Simulate, version, ship
Test with realistic conversation simulations. Every change is versioned with full metrics history. Promote the winning config to production with one click - and roll back instantly if needed.
Powering teams from
prototype to production
From ambitious startups to global enterprises, teams trust Future AGI to ship AI agents confidently.