Agents do not have long-term memory
They look smart inside one chat, then forget goals, preferences, project context, and historical decisions in the next task.
Back to business lines[ AGENT EFFICIENCY ARCHITECTURE ]
Agent Efficiency Architecture
Memory, collaboration, and harness on top; concurrency, microservices, and cloud-native engineering underneath.
Agents are not model scripts. We design around memory, multi-agent collaboration, harness runtime, and high-concurrency / microservice / cloud-native foundations so agent systems can run, trace, review, and scale.
[ WHEN IT FITS ]
When This Line Matters
Multiple agents collaborate poorly
Research, planning, execution, review, and recap roles lack boundaries, shared context, and reliable handoff.
The demo runs, but production is uncontrolled
Without a harness layer, tools, permissions, memory, logs, evaluation, rollback, and human fallback are scattered.
Model calls are slow and expensive
Longer context, more tools, queues, cache, rate limits, retries, and LLM gateways are not designed as one governable path.
Tool and microservice boundaries are messy
MCP, internal APIs, databases, queues, and third-party services are coupled, making failures hard to diagnose.
The cloud-native runtime is not ready
Agents need long jobs, concurrency, streaming, mixed GPU / CPU resources, and recovery, but the deployment still behaves like a normal web service.
[ CASE PATTERNS ]
How the Capability Shows Up in Real Work
Four-layer separated memory architecture
Context
The agent repeatedly lost long-term goals, preferences, project background, and previous decisions, relying only on the current context window.
Intervention
Separated memory by granularity: profile / goal memory, project / domain memory, task / session memory, and atomic event / tool-trace memory; each layer has its own write, retrieval, update, conflict, and permission policy.
Outcome
Created a GPT-like memory experience where the agent knows who the user is, what the project is, where the last task stopped, and why each step happened.
Multi-agent collaboration architecture
Context
Complex work required research, planning, execution, review, and recap roles, but naive agent chaining caused context pollution, duplicate work, and unclear ownership.
Intervention
Designed role boundaries, shared memory, handoff contracts, conflict arbitration, review checkpoints, and recap loops.
Outcome
Delivered multi-agent topology, role prompts, shared-context protocol, and task state machine to reduce the risk of one oversized agent doing everything.
Agent harness runtime framework
Context
The prototype could run, but tool calls, permissions, logs, memory writes, evaluation, and human fallback were scattered across code.
Intervention
Built a harness architecture around models, prompts, tool adapters, memory layers, permissions, evaluation, observability, replay, and fallback strategy.
Outcome
Moved agents from script-like demos into an engineering runtime that is debuggable, auditable, reviewable, rollbackable, and ready for multi-agent collaboration.
High-concurrency Agent / LLM gateway refactor
Context
As model calls and agent tasks grew, request queues, token cost, context construction, tool waits, and streaming latency all increased.
Intervention
Redesigned the LLM gateway, request queues, context cache, result cache, streaming output, rate limits, fallback, retries, and cost attribution.
Outcome
Delivered a high-concurrency call-chain topology, caching and queue strategy, cost observability model, load-test baseline, and fallback plan.
Microservice and tool-call governance
Context
Agents called MCP tools, internal APIs, databases, search services, and third-party platforms, but failures were hard to classify.
Intervention
Clarified tool boundaries, service contracts, call protocols, error taxonomy, permission model, trace IDs, and replay mechanisms.
Outcome
Created tool registration standards, service-chain observability, error classification, and replay debugging flow.
Cloud-native agent runtime foundation
Context
Agent workloads mixed short requests, long jobs, batch processing, streaming, and concurrent tool execution, which a single web service could not handle cleanly.
Intervention
Separated web entry, workers, scheduler, memory service, tool service, and observability service with container orchestration, resource isolation, recovery, and scaling strategy.
Outcome
Delivered cloud-native runtime topology, service decomposition, autoscaling strategy, job recovery, and deployment / rollback path.
[ CORE CAPABILITIES ]
What We Actually Solve
Four-layer memory architecture
Separate profile, project, task, and event memory by granularity, with policies for write, retrieval, update, forgetting, and conflict.
Multi-agent collaboration
Design roles, task states, shared memory, handoff contracts, review checkpoints, and conflict arbitration.
Harness runtime framework
Wrap models, prompts, tools, memory, permissions, evaluation, logs, replay, and human fallback into an operable runtime layer.
High-concurrency performance governance
Govern LLM gateways, queues, context cache, result cache, rate limits, fallback, streaming, and cost attribution.
Microservice and tool-chain governance
Clarify MCP, internal APIs, databases, search, third-party services, and permissions so tool calls can be traced and replayed.
Cloud-native runtime foundation
Design web entry, workers, schedulers, memory services, tool services, observability, isolation, scaling, and recovery.
[ DELIVERY PATH ]
From Diagnosis to Implementation
Architecture audit
Review the agent prototype, business systems, tool chain, model calls, deployment, logs, and monitoring to locate true bottlenecks.
Memory modeling
Map what the agent must remember about people, projects, goals, tasks, decisions, events, and tool calls.
Collaboration topology
Define role boundaries and handoffs between single agents, multi-agent teams, human nodes, and tools.
Harness implementation
Implement runtime shell, tool adapters, memory I/O, permissions, logs, evaluation, and fallback strategy.
Engineering foundation optimization
Optimize LLM gateway, queues, caches, microservice boundaries, cloud-native deployment, recovery, and cost governance.
Sample validation
Use real task samples to validate memory hits, collaboration efficiency, tool success, latency, cost, and recovery.
[ DELIVERABLES ]
The Output Is Executable Assets, Not Loose Advice
Four-layer memory architectureMulti-agent collaboration topologyHarness runtime planHigh-concurrency call-chain optimizationMicroservice and tool governance checklistCloud-native runtime topologyAgent observability and evaluation metrics
— CONTACT
Contact Us Move agents from demos into long-term engineering systems
For teams building agents, AI workspaces, enterprise assistants, knowledge assistants, or multi-agent automation with memory, collaboration, performance, cost, and operability challenges.