Progress Agent Engineering: Observability
Trace and Observe AI Agent Behavior
Your agent returned an answer. Now see how it got there.
See the full AI execution path across prompts, model calls, tools, and retrieval for AI agents and LLM applications so you can understand how every response is generated and know where to investigate next.
No credit card required to start. 5 minute set up.
AI Agents Can Look Right While Going Wrong.
Agents can return polished answers while using the wrong context, skipping a tool, looping through expensive calls, or failing silently. Turn each run into evidence your team can inspect, including activity from MCP-connected tools and workflows.
See the Run
Trace the full path from user request to final answer across prompts, models, tools, retrieval and workflow steps.
Explore MoreFind the Break
Pinpoint the failed span, skipped tool, weak context, slow step or costly call behind bad agent behavior.
Explore MorePrevent Repeat Failures
Move from one broken run to fixing recurring trends across releases, workflows, costs and production traffic.
Explore MoreCapabilities
Explore MoreSee the Run
Trace every agent response from request to answer
See trace, status, latency, token usage, and estimated cost before deciding what to fix.
- View prompts, spans, model calls, tools, and retrieval in one trace.
- Compare status, latency, tokens, and cost across runs.
- Use Agent View to investigate all agent runs from one place.
Find the Break
Diagnose Failures Fast
Tie failures to the exact span, context, and response that caused them.
- Find failed spans, skipped tools, retries, loops, and handoff issues.
- See whether a problem came from a prompt, model call, retrieval step, MCP server, or workflow span.
- Connect weak or hallucinated answers to the context and sources that shaped them
Prevent Repeat Failures
Spot recurring issues and regressions before they become risk.
Production patterns reveal which workflows, releases and cost drivers need attention before one failed trace becomes a broader issue.
- Filter by app, environment, release, customer, tag, or status.
- Spot recurring failures, latency regressions, and cost spikes.
- Use AI clients through MCP to summarize traces, review scores, and analyze cost without exposing production systems
Capabilities
Observability Across the Agent Lifecycle
Turn every agent run into trace-connected evidence your team can inspect, debug and optimize, including model calls, tool use, retrieval, latency, token usage, cost and workflow behavior.
Trace Visibility
See the full execution path across prompts, model calls, tools, retrieval and output.
Retrieval and Grounding
Connect answers to the retrieved sources, context and prompts that shaped them.
Production Patterns
Find recurring failures, quality drops, cost spikes and release regressions.
AI Workflow Debugging
Pinpoint failed spans, retries, loops, skipped tools and handoff issues.
Cost Visibility
Attribute tokens, latency and estimated cost to the spans and workflows that created them.
MCP Servers
Bring observability data into the AI clients developers already use with scoped, read-only access.
AI Improvement Loop
Turn Production Evidence Into
Continuous Improvement
Design with Control. Observe what happened. Evaluate the outcome. Improve continuously.
Connect Your Agent.
Paste this prompt into your agent.
Sign up for free · No credit card required
Connect Your Agent Paste this prompt into your agent
Install https://github.com/telerik/observability-skillsWorks with Cursor, Claude Code, Copilot, Codex and more.
Set up API Key
Your agent will ask for your API key. Get your API Key here.
Pricing
Simple, predictable pricing. Start free, scale as you grow. No surprises, no hidden fees.
Free ForeverFor developers testing early agent prototypes
per month
Includes 10,000 units
Retention: 7 days
StarterFor small teams deploying their first live AI agents
per month
Includes 200,000 units
Retention: 30 days
$8 USD per additional 100K units
ProFor teams running production AI agents at scale
per month
Includes 1,000,000 units
Retention: 60 days
$8 USD per additional 100K units
EnterpriseFor organizations scaling governed AI applications
per month
Custom trace volume
Retention: Infinite
Explore More in Progress Agent Engineering
Evaluations
Design, Test and Improve Agent Behavior
Use prompts, playgrounds, datasets, experiments and LLM-as-a-judge evaluations to compare behavior before and after release, track regressions and optimize for quality, safety and cost.
Governance
Keep Agents Inside Approved Boundaries
Use model gateways, access controls, auditability, human review, alerts, notifications and policy controls to manage cost, quality, risk and security.
Memory & Context
Control What Agents Retrieve, Remember and Reuse
Manage what agents retrieve, remember, reuse, share and forget across workflows so they use the right context in the right place.
Agent Engineering Overview
See the Full Control Layer for Production AI
Explore how Progress Agent Engineering brings observability, evaluations, governance and memory/context together to help teams improve quality, cost and trust in production AI.
Works with Your Stack
The Progress Agent Engineering Platform integrates with the tools, frameworks and platforms teams already use to build and run AI agents.
- Languages & SDKs: .NET (C#), Python, JavaScript/TypeScript
- Agent Frameworks: Semantic Kernel, LangChain, LlamaIndex, AutoGen, Microsoft Agent Framework
- LLM Providers: Azure OpenAI, OpenAI, Anthropic
- AI Tooling: Microsoft.Extensions.AI, Microsoft AI Foundry, Progress RAG
- Enterprise SSO: Okta, Azure AD, SAML
- Open-Source Models (OSS): Llama 2/3, Mistral, Mixtral, Falcon, Gemma, etc.
"We cut our agent debugging time from 4 hours to 20 minutes. Being able to see the full trace - prompts, retrieval, tool calls - in one view changed how our team works."
Early Access Program Participant
Frequently Asked Questions
Stop Guessing What Your Agents Did
Start tracing prompts, model calls, retrieval, tools, latency, tokens, cost and outputs in minutes.
Debug faster, explain production behavior and ship better agent releases with evidence.