Autonomous Agents
Agents that improve with every run instead of starting over
Give agents durable memory of outcomes, discovered environment facts, and failed approaches, so long-horizon work stays coherent and mistakes are not repeated.
The problem
A stateless agent begins every run knowing nothing about the runs before it. It cannot learn that an endpoint returns misleading errors, that a step needs a flag on this project, or that the approach it is about to attempt has already failed four times. It re-derives everything, and re-derivation costs tokens, time, and sometimes an irreversible side effect.
With MemorySync
MemorySync gives the agent a durable record of conclusions — outcomes with their reasons, environment facts learned the hard way, and approaches ruled out. Retrieved before planning and before consequential steps, that record narrows the search space instead of leaving it constant.
How it works
- 1
Recall
Before planning, the agent retrieves prior outcomes and standing conventions.
- 2
Check
Before an expensive or irreversible step, it checks what happened last time.
- 3
Act
The agent executes with the search space already narrowed.
- 4
Record
The conclusion and its reason are written back — especially on failure.
Re-derivation is the whole problem
An agent without memory is a capable function call with no ability to improve on its previous invocation. Every run rediscovers the same environment quirks and re-attempts the same dead ends.
That shows up as cost and as unpredictability. The same task takes a different number of steps each time, because nothing accumulated.
Store conclusions, not traces
The instinct is to store every step and observation. That produces a corpus that is bulky and unusable at retrieval time, for the same reason storing a whole chat transcript is unusable.
The useful unit is the conclusion with its reason:
- Outcomes and why. *"Deploy fails at the default worker count; this service needs at least four."*
- Environment facts. Which endpoint is authoritative, which credential holds which scope.
- Approaches ruled out, with the reason. Without the reason, a later run cannot tell whether the failure was inherent or incidental.
- Decisions and justification. Long-horizon work is a chain of decisions, worthless without the reasoning.
A failure with a recorded reason is the highest-value memory an agent can hold. A failure without one is noise.
Separating memory shapes
Agents use memory in ways that need different retrieval treatment, and conflating them is a common design error.
- Working state — the current plan and progress. Retrieved in full, because the agent needs all of it.
- Episodic memory — prior runs. Retrieved by relevance to the current step.
- Shared memory — the pool several agents read, so a researcher's finding reaches an executor.
- Conventions — standing rules, retrieved on essentially every run.
Project and end-user scope in MemorySync separates these without running several stores.
Multi-agent coordination needs real isolation
When agents act on behalf of different customers, one agent must not read another's memories, and a narrowly-scoped agent should not inherit a broader one's context.
That is a scoping requirement rather than a convention. MemorySync enforces it structurally, beneath the query.
Measuring whether memory helped
Agents make this easy, because the same task can be run repeatedly. Track steps to completion across runs, repeated-failure rate, and tokens per completed task. With memory working, all three should fall. If tokens rise, you are storing traces instead of conclusions.
What you get
- Failed approaches are not retried
- Long-horizon tasks survive the context window
- Environment facts are learned once
- Fewer steps per run over time
- Retrieval is inspectable when a decision was poor
- Per-agent and per-project scope
Use cases
Build autonomous agents with durable memory
Start on the free tier and test retrieval, scoping, and deletion against your own workload.
Start free. No credit card required.