AI Engineering Solutions

Build, run, and improve reliable AI agents.

Building AI agents is one challenge. Running them reliably, debugging failures, controlling costs, and improving behavior over time is another. Progress gives AI engineers the tools to control the process.

The AI Engineering Cycle Presents New Challenges

The non-deterministic nature of AI systems creates barriers to successful initiatives. Outputs can be hallucinated, regulations for PII, HIPAA and accessibility can be broken and token usage can spiral out of control often without warning.

Stage 1

Build & Test

  • Redundant LLM calls and cost inflation
  • Ambiguous goals & planning errors
  • Role overreach & scope drift
  • Poor task decomposition
Stage 2

Deploy & Observe

  • Unexpected token spend
  • User-facing hallucinated outputs
  • PII, HIPAA & accessibility violations
  • Execution & tool-use failures
  • Silent verification failures

solution:

AI Observability
Stage 3

Optimize & Improve

  • Emergent multi-agent failures
  • Cascading prompt collisions
  • Recurring agent mistakes
  • No model lineage or dependency tracking
available

AI Observability

See inside every agent decision: prompts, tool calls, cost, latency and quality in one trace.

Works with .NET, Python, JavaScript and all major LLM providers.

Features
  • Trace and Observe: End-to-end trace across multi-step and multi-agent workflows
  • Debug: Real-time alerts, anomaly detection, performance degradation and root cause analysis
  • Control Costs: Token usage, cost tracking and per-call latency
  • Evaluate Quality: Hallucination detection and response quality scoring
available

Agent Cache

Reduce redundant LLM calls and cut token costs during development and in production.

Features
  • Semantic cache matching for LLM request deduplication
  • Configurable TTL and cache invalidation policies
  • Real-time cost savings and hit-rate dashboards
  • Drop-in integration—no agent code changes required

Part of Fiddler Everywhere.

coming soon

Agent Memory

Give AI agents the ability to store, retrieve, and reason over past interactions — so behavior improves over time rather than resetting with every session.

Features
  • Persistent memory stores across agent sessions and users
  • Selective recall—surface relevant past context at inference time
  • Reduce repeated failures by learning from prior decisions
  • Improve personalization and task continuity without fine-tuning
  • Governance controls over what is stored, retained and forgotten
Request a Demo

Works Well With Progress Agentic RAG

Progress® Agentic RAG platform is a high-value SaaS solution that automatically indexes files and documents to fuel diverse use cases with LLMs and AI Agents, offering high-quality outputs through RAG quality metrics. And it can be monitored via our AI Observability platform.

40 Years Building the Tools Developers Trust

We're not new to the infrastructure layer. Progress has spent four decades deeply embedded in the software development process and we're bringing that same enterprise-grade reliability to the AI engineering stack.

Already Inside 24,000+ Enterprise Teams

Over 24,000 development teams use Progress tools today. We are deeply in tune with the needs and workflows of professional development organizations.

AI-Driven Tools Across the Development Lifecycle

Our collection of AI-driven developer tools already helps teams improve productivity and embed AI features for their end-users. Agent tooling is the natural next layer.

Built for Compliance From the Start

Enterprise teams don't just need AI that works, they need AI that complies. Progress has spent decades helping teams ship innovative, accessible and regulation-ready applications.

Take Control of Your AI Engineering Stack

From visibility to cost control to continuous improvement—Progress helps you build, run, and scale reliable AI agents with confidence.