Progress Agent Engineering: Memory & Context
Help Your AI Agents Remember What Matters
AI agents need memory, not baggage.
Give agents persistent memory and trusted RAG context so future answers stay relevant, trusted and safe.
No credit card required. Free for small teams. Set up agent memory support in minutes.
Memory Without Control Creates Risk.
Agents fail when they remember the wrong thing or retrieve the wrong source. Keep context relevant, trusted and safe to reuse.
Store What Matters
Capture durable user preferences, facts and prior decisions without forcing users to repeat themselves.
Explore MoreRecall the Right Context
Retrieve the most relevant memory and trusted RAG context before the agent answers.
Explore MoreKeep Memory Under Control
Inspect, tune and remove stale or unsafe memory before it influences future responses.
Explore MoreStore What Matters
Give agents useful continuity
Store durable facts, preferences, constraints and decisions as structured memory across sessions.
- Capture memory from real conversations
- Search extracted memories by user, agent or use case
- Add persistent memory with SDKs and APIs
Recall the Right Context
Retrieve the context each answer should use
Use semantic recall and trusted RAG context to bring the right memories, sources and policies into the agent's next response.
- Find relevant memory automatically.
- Return sources, attribution and confidence.
- Ground responses in trusted RAG, memory, attribution, confidence and trace context.
Keep Memory Under Control
Remove stale memory before it creates drift
Inspect what agents remember, tune what gets recalled and remove outdated or unsafe context.
- Browse conversations and extracted memories from one workspace
- Filter memory by agent, user, status, category or search query
- Mark memories active, outdated or removed
- Tune recall without deleting source history
Capabilities
Memory and context capabilities across the agent lifecycle
Persistent Memory
Store useful facts, preferences and prior decisions across sessions.
Memory Lifecycle
Mark memories active, outdated or removed as context changes.
Developer Integrations
Add memory to agent workflows with SDKs, APIs and scoped access.
Semantic Recall
Retrieve relevant memory with source attribution and relevance scoring.
Memory Workspace
Browse conversations, extracted memories, processing status and usage.
RAG and Context Grounding
Combine persistent memory with trusted sources and policies.
AI Improvement Loop
Turn Memory and Context 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.
Learn & Explore More
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
Observability
Observe and debug production behavior
Trace prompts, model calls, retrieval, tools, workflow steps, latency, tokens and cost so teams can understand what agents actually did.
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.
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.
Frequently Asked Questions
Stop Feeding Your Agents Stale Context
Give your agents persistent memory and trusted RAG retrieval, with controls to keep outdated information out of future responses.