AI agent memory, engineered for production.

AI agent memory is the layer that lets an agent remember facts about a user or task across sessions, because LLMs on their own are stateless. Awana designs and builds that layer for production agents: a write path that saves what matters after each conversation, and a read path that recalls it before each model call.

RAG retrieves from documents you wrote; agent memory is written by the system from live conversations, so it changes constantly and can contradict itself. Awana engineers add the deduplication, versioning, conflict resolution and forgetting that memory needs on top of your existing RAG.

Most production agents need five: working memory within a task, semantic memory of your reference knowledge, episodic memory of past sessions, procedural memory of how work gets done, and personal memory of each user's preferences. Awana designs all five and starts with episodic and personal memory, where agents fail first.

Deterministic memory stores exact values, like a number a user chose or the output of a previous step, and recalls them with plain code instead of asking the model. Awana uses it for anything with one right answer, so the value is identical every time and carries its source for audit, and leaves judgment calls to the model.

No. Awana adds memory beside what you already run, so your vector store, LangChain or LlamaIndex setup stays. Our engineers add a write step after each session and a recall step before each model call, inside your repo.

Yes. The Awana memory audit starts from what you run today, measures it against a golden set, and keeps what works. Awana engineers migrate the memories you already store into versioned, per-customer records rather than discarding them.

Yes. Awana builds AI agent memory on Microsoft Azure and AWS, inside your own cloud account, with Azure OpenAI, Amazon Bedrock, or the OpenAI and Anthropic APIs called directly. The memory layer runs where the rest of your agent runs.

No. Our partner platform, Augenta, is one option. Awana can also build the memory layer on Postgres and pgvector in your cloud account, so all memory data and code stay yours with no platform license, and Awana recommends an approach in the audit.

The read path adds one scoped lookup before each model call, and writes happen asynchronously after the session. Awana agrees a latency budget with you in the audit and measures it on your traffic at every release.

Awana builds a golden set from your real conversations, and calibrated human evaluators grade recall precision, staleness, forgetting and cross-user leakage blind. Awana measures inter-rater agreement and calibrates an LLM judge against the humans, so every release passes a gate before rollout.

Awana scopes every memory to a tenant and a subject and filters recall on that scope before ranking. Awana evaluators run red-team cases every release that try to pull one customer's data into another's session, and nothing ships if one of those cases fails.

Awana signs a standard DPA and NDA before any data is shared, background-checks every engineer and evaluator, and grants access through your own SSO or OAuth with least privilege. The memory store, golden sets and grading tools stay in your cloud account, and PII is masked before any person reviews a sample.

Yes. Awana is United States-led, and its leadership holds an active US security clearance. For public-sector work, Awana scopes staffing to the agency's requirements, including US-based personnel where required.

You own everything Awana builds: the code in your repo, the memory data in your cloud, the golden sets and rubrics, and a runbook for your team. Each release gate also produces a report with the golden-set version, scores per metric, rater agreement and the pass or fail decision, which your audit and model-risk teams can keep.

With Awana, most teams are live about six weeks after the first call: a two-week memory audit, then about four weeks to build, test and roll out behind a release gate. Regulated companies should add the time their own security review takes.

Usually, because the prompt carries a few relevant memories and the last few turns instead of the full conversation history. In the memory audit, Awana measures tokens per turn on your real traffic before and after, so you see the actual effect before committing to a build.

Awana works hourly or as a fixed-price project. The memory audit and the implementation are fixed-price projects scoped to your agent, and ongoing evaluation and engineering are billed hourly. Awana gives you a quote after the first call.

Awana implements and tests AI agent memory for teams building agents without a dedicated research group. Awana is United States-led, pairing its engineering leadership with a cost-effective Latin America team of engineers and calibrated evaluators, working in US time zones inside your repo, cloud and tools under NDA, hourly or as a fixed-price project.