Your agent is 80% plumbing. Context, memory, orchestration, reliability, security. The model call is the easy part.
The agent harness your team shouldn't have to build.
Three-tier context compaction. Managed memory extraction. Durable checkpoints. Distributed coordination. You bring your skills. We run the harness.
Agents that demo well break in production.
The model call is 20% of a production agent. The other 80% is the harness that makes it actually work. Most teams never build it.
Context degrades silently
Context windows fill up mid-task. Your agent forgets what it was doing. Naive compaction either drops critical information or burns tokens retrying thousands of times. One leaked implementation failed 3,272 consecutive compaction attempts in a single session before anyone noticed.
Memory resets every session
Million-token windows create an illusion of memory while degrading retrieval quality. Without structured fact extraction and curation, your agent starts every conversation with structural amnesia. It's not an intelligence gap. It's a memory gap.
The harness is 80% of the work
Session persistence, credential management, crash recovery, token budgets, tool registries, observability, audit logging, process isolation, distributed coordination. Twelve infrastructure primitives, each one a full engineering project. That's not what you signed up for.
The harness, built and managed.
You bring your skills and integrations. We provide the production infrastructure that makes them work at scale.
Three-tier context compaction
Tier 1: mechanical observation masking replaces stale tool outputs with compact placeholders — zero LLM cost. Tier 2: when token usage hits 80% capacity, adaptive LLM summarization compresses middle segments while preserving task semantics. Tier 3: emergency truncation as a safety net. Full token accounting across all tiers.
Managed memory extraction
Not just "we store conversations." A dedicated memory worker uses the LLM to extract structured facts — preferences, decisions, corrections, entities — stores them in DynamoDB with TTL, and auto-trims to budget. Memory that curates itself.
Elastic runner infrastructure
Agents run as demand-driven goroutines, not long-lived containers. Scales from zero to thousands of concurrent agents. 60-second idle cleanup. DynamoDB-based distributed leases coordinate across multiple runners — no consensus protocol, survives network partitions, enables rolling restarts with zero coordination.
Durable task resumption
Checkpoint to durable storage after every LLM call. If the runner restarts mid-task, your agent picks up exactly where it left off — same message history, same context, same task. Transparent recovery. The user never knows anything happened.
Hierarchical agent orchestration
Pipeline engine runs DAG-ordered phases with topological sorting. Within each phase, sub-agents execute in parallel with semaphore-controlled concurrency. Separate sessions, filtered tool registries, role-specific prompts. Production orchestration, not a demo.
Three steps to a running agent.
No infrastructure setup. No six-month build. No hiring a platform team.
Define
Tell us what your agent should do. Bring your skills, your integrations, your workflows. We work with you to scope the right hosting setup for your needs.
Deploy
We set up memory, context management, and isolated infrastructure for your agent. Your skills and integrations plug in. Deployed in days, not months.
Run
Your agent is live. You use it. We keep it running — monitoring, patching, scaling. You get the value. We handle the ops.
Built for reliability, not demos.
"A probabilistic system with constraints becomes a reliable machine."
Decoupled services
Runner (compute), tool gateway (tools), memory worker (context/facts) are independent services.
Tools update without restarting agents. Memory tunes without touching the runner. Each piece scales independently.
Skills runtime
Your skills run in a versioned runtime with preflight optimization — an LLM call selects only the skills needed for the current task, keeping the system prompt lean.
Hierarchical discovery, frontmatter metadata, lazy loading. You write the skills. We run them.
Observable by default
Cumulative metrics on compression events, tokens saved, masking count, lease renewals, pipeline step timings.
Per-agent token accounting. You know what your agent did, how much it cost, and whether it worked.