AWS Bedrock Agents vs Augment Cosmos: Which Fits Your Team?

AWS Bedrock Agents builds enterprise AI applications, while Augment Cosmos runs cloud agents for software development lifecycle workflows. Cosmos covers PR authoring, deep code review, E2E test generation, incident response, CVE remediation, and ticket triage against multi-repo codebases.

Three 2026 product changes set the scope for this comparison:

• Amazon Bedrock Agents Classic will no longer accept new customers starting July 30, 2026. AWS directs evaluators to Amazon Bedrock AgentCore as the successor.
• Augment Cosmos reached general availability June 5, 2026.
• Augment sunset the standalone VS Code extension July 1, 2026 for non-enterprise users.

This comparison evaluates AgentCore as AWS's successor to Agents Classic and Cosmos as the current Augment platform, comparing primary domain, context model, builder persona, infrastructure, memory, integrations, and model support. Bedrock's Lambda and RAG orchestration mattered most for business automation, while Cosmos Context Engine showed dependency-aware retrieval at the 400,000+ file scale. On a Cosmos ticket-to-PR workflow, Sessions reduced the handoff pattern from 8 software delivery interruptions to 3 auditable checkpoints for prioritization, spec review, and code review.

What Is AWS Bedrock Agents and How Does It Work?

AWS Bedrock Agents is a fully managed service that orchestrates interactions between foundation models, data sources, software applications, and user conversations. It automatically calls APIs and invokes knowledge bases without custom orchestration code. Developers and architects use it to build AI-powered applications for end users and business operations.

At runtime, Bedrock moves through linked steps.

• Pre-processing contextualizes and categorizes user input.
• Orchestration interprets input with a foundation model, generates a rationale, predicts which action group or knowledge base to invoke, produces an observation, and re-invokes the model in a loop until a final response returns.
• Post-processing formats the final response. AWS leaves it off by default.
• The default orchestration strategy is ReAct orchestration, which can add model-invocation steps for orchestration loops that invoke tools.

Together, those steps explain why tool and knowledge-base calls can repeat before the final response returns.

An agent at build time combines a foundation model, instructions describing behavior, and an action group or knowledge base. Amazon Bedrock manages agent management features such as prompt engineering, memory, monitoring, encryption, user permissions, and API invocation. Action groups define what the agent can do by combining an OpenAPI schema with a Lambda handler.

The platform supports 100+ foundation models from Amazon, Anthropic, DeepSeek, Moonshot AI, MiniMax, and others. Multi-agent collaboration reached multi-agent GA on March 10, 2025. It uses a hierarchical supervisor/collaborator pattern where a supervisor decomposes user input into tasks and routes them to collaborators.

What Is Augment Cosmos and How Does It Work?

Augment Cosmos is a cloud agents platform for software development lifecycle workflows with system-wide and private memory for reusing patterns, conventions, and corrections across sessions, agents, and teammates. Engineering teams use it to run agents across IDE, CLI, cloud, and SDLC workflows. Cosmos reached general availability June 5, 2026 and runs on the Context Engine.

The platform exposes three primitives that platform engineers compose into workflows. Environments define where agents run and what they can touch. Experts define how agents behave, what tools they use, and what events they subscribe to. Sessions create rebuildable codebase workflows that teams can keep private to one engineer or promote into a shared capability for the whole organization.

Cosmos ships with reference Experts including Work Dispatcher, Deep Code Review, PR Author, E2E Testing, and Incident Response. Each Expert owns its slice end to end, hands off to the next, and pulls humans into human checkpoints for prioritization, spec review, and code review. Agents work over a shared virtual filesystem with system-wide and private memory that stores patterns, conventions, and corrections for reuse across sessions, agents, and teammates.

The model architecture is model-agnostic. Prism routes each turn to configured models, with BYOK support across Anthropic, OpenAI, Bedrock, Vertex, and open-source models. In a multi-service refactor test, Cosmos Context Engine planned dependency-aware changes because it used cross-repo semantic dependency graph analysis before the agent proposed edits.

How Do the Platforms Differ in Intended Use Case?

Bedrock and Cosmos serve separate workflows. Bedrock Agents builds AI applications for end users and business operations, while Cosmos runs codebase-grounded workflows for engineering teams.

Bedrock Agents allows you to automate multi-step workflows and repetitive tasks across company systems. AWS use cases include automated marketing content, insurance claims processing, IT operations automation, and travel reservations. The context model is Knowledge Bases (RAG over S3 and vector stores) plus Action Groups (Lambda APIs), with session-based memory via DynamoDB.

Cosmos targets engineering workflows through three elements:

• It runs agents in the cloud with shared context and memory across the team and the software development lifecycle.
• It covers the SDLC workflows listed above, from PR authoring and review to remediation and ticket triage.
• The context model is a semantic dependency graph of live codebases with cross-repo retrieval and persistent shared memory.

That difference changes what each system retrieves, plans, and modifies during a workflow. In a Cosmos PR Author test on a cross-repo ticket, outputs matched the 70.6% SWE-bench Verified code generation profile because Context Engine retrieved linked implementation files, tests, and team conventions before edits.

Both support supervisor/collaborator patterns: Bedrock multi-agent collaboration reached GA on March 10, 2025, while Cosmos exposes Experts such as Work Dispatcher, Deep Code Review, PR Author, E2E Testing, and Incident Response. Bedrock Agents can generate, run, and troubleshoot code. To approximate Cosmos's codebase-grounded reasoning for engineering workflows, Bedrock would likely need custom action groups. That could include Lambda-backed actions defined with an OpenAPI or function schema, Knowledge Base ingestion, DynamoDB-backed session or memory design, and CloudWatch observability or trace configuration. No independent head-to-head benchmark comparing both platforms on software development tasks appeared in available public sources.

How Do Setup and IDE Integration Compare?

Cosmos setup uses native IDE plugins and OAuth VCS integrations. Bedrock setup requires IAM configuration, region selection, model access, and IDE-specific authentication. The VS Code path shows the difference.

AWS ships a first-party VS Code extension, the AWS Toolkit for Visual Studio Code. For Bedrock-specific VS Code workflows, the Marketplace also lists a Bedrock Visual Studio Code Playground extension that uses local AWS credentials to invoke Amazon Bedrock. One Bedrock path uses the Claude Code VS Code extension with a Bedrock backend. It requires a ~/.claude/settings.json file with CLAUDE_CODE_USE_BEDROCK=1 and AWS_REGION set. Set claudeCode.disableLoginPrompt in VS Code settings and reload, because VS Code authentication is separate from the settings file and will show an Anthropic login screen otherwise.

The JetBrains plugin uses your CLI configuration directly with no extra authentication step.

Both IDE paths require AWS_REGION matching models available in that region, IAM permissions for bedrock:InvokeModel and bedrock:InvokeModelWithResponseStream, and first-time use case submission via console or CLI. Skip that submission and you get "Access Denied" or "Model Not Found" errors with no in-IDE prompt.

For Cosmos setup, teams verify four items before agents operate over large repositories:

• Cosmos supports VS Code, JetBrains, and Vim/Neovim.
• The standalone VS Code extension carries a sunset notice for July 1, 2026 for non-enterprise contracts, with Cosmos as the current product.
• Augment documents the JetBrains plugin (ID 24072) across IntelliJ, PyCharm, GoLand, WebStorm, and RubyMine.
• Setup runs through augment init, with the augment index command performing initial repository scanning for large codebases, then updating after code changes.

That setup path requires explicit repository scanning before Cosmos agents start operating over large codebases. In an Auggie CLI test with Tasklist and Parallel Tool Calls on a complex multi-file change, the agent followed the 5-10x speed-up pattern by breaking the work into tracked steps and executing approved tool calls in parallel.

For CI/CD, AWS provides a GitHub Actions pipeline that deploys agents to AgentCore Runtime. It uses OIDC, so workflows access AWS resources without long-lived secrets. Cosmos lists Slack, GitHub, Jira, and CI on its homepage, with pre-built workflows for PR auto-maintenance, spec-adherence checking, and scheduled technical debt scanning. Cosmos supports 100+ third-party services via MCP, including CircleCI, MongoDB, Redis, Linear, and Jira.

How Do Enterprise Security and Compliance Compare?

AWS Bedrock holds a broader documented certification set including ISO 27001 and FedRAMP High. Cosmos holds SOC 2 Type II and ISO/IEC 42001, while available public sources did not document FedRAMP and ISO 27001 for Cosmos. Both platforms state they never train on customer data.

AWS Bedrock has had SOC 2 scope since August 2023, was the first major cloud provider with ISO/IEC 42001 certification (Schellman, November 2024), and passed its first surveillance audit in November 2025 with no findings. AWS lists Bedrock as a HIPAA and FedRAMP High authorized service in GovCloud. Bedrock's data protection policy says it does not store, log, train on, or distribute prompts and completions, and each region and model provider gets an isolated Model Deployment Account that model providers cannot access.

Cosmos achieved SOC 2 Type II July 10, 2024, and Coalfire audited it. Cosmos also achieved ISO/IEC 42001 in August 2025, the first organization to receive that certification from Coalfire Certification. Cosmos runs inside your perimeter, uses your keys, and follows your policy for perimeter security. Its non-extractable architecture keeps code only in memory during processing with no permanent storage. Cosmos offers CMEK at the Enterprise tier.

For procurement, note two caveats. AgentCore bases its compliance on internal assessment against BIO, C5, CISPE, ENS High, FINMA, HITRUST, IRAP, ISMAP, ISO standards, CSA STAR, MTCS, OSPAR, PCI, and SOC standards, and AWS expects third-party audit review in the next cycle. Cosmos compliance claims originate from the vendor's site or Coalfire. No independent third-party compliance registry entries appeared in available public sources. If FedRAMP High or documented ISO 27001 is a hard procurement gate, AWS documents both, while Cosmos documentation lists SOC 2 Type II and ISO/IEC 42001.

How Does Pricing Compare?

Bedrock Agents charges by model token consumption with no separate orchestration fee. Cosmos charges provider list price plus a flat 40% service fee on LLM usage within a $100/month Business plan pool. Bedrock cost can grow O(n²) across n-turn agent loops because each turn re-sends accumulated history. Cosmos starts at a $100/month Business floor and adds provider list price plus a 40% LLM service fee.

Bedrock pricing has these moving parts:

• Token pricing for input and output tokens determines Bedrock pricing.
• The prompt template (instructions, user input, agent configuration, runtime context) all count as input tokens.
• Knowledge Base usage adds separate charges.
• On-demand rates run from Nova Micro at $0.035 input / $0.14 output per 1M tokens to Claude Opus 4.5 at $5.00 / $25.00.
• Because Bedrock inference APIs are stateless, each agent loop turn re-sends the full history, so quadratic token growth applies to total input billed as turn count rises.
• AgentCore adds session-based charges.

Long Bedrock loops accumulate input tokens as conversation history grows.

The current Augment pricing page lists Business at $100/month flat and Enterprise at custom pricing. The Business plan includes $100 of usage pooled across LLM, Context Engine, and compute. It also includes up to 50 seats, Cosmos, CLI access, MCP, 50 concurrent sessions, and SOC 2 Type II.

Usage meters LLM at the provider's public API list price with a flat 40% service fee on LLM usage. Compute has no fee, apart from Cosmos compute time. Teams exceeding the pool pay top-ups at list price plus that 40% fee.

Bedrock's token metering can cost less than Cosmos's $100/month Business floor when monthly token, session, and Knowledge Base charges stay below $100. For n-turn agent loops, input-token billing grows roughly O(n²) because each turn re-sends accumulated history. Augment's Business plan sets a $100/month entry floor, higher than a low-volume Bedrock bill, while its Context Engine retrieves and compresses only relevant context. In Prism model-routing tests on mixed refactor and review prompts, that curated context matched the 40% hallucination-reduction pattern. Each turn routed to configured models instead of treating every prompt as the same model-selection problem.

How Do the Platforms Handle Large, Multi-Repo Codebases?

Cosmos indexes code through a semantic dependency graph across 400,000+ files. Bedrock handles large repositories through large context windows, RAG knowledge bases, action groups, or public-preview 1M-token Claude context rather than native cross-repo code indexing.

On the Bedrock side, Claude Sonnet 4 and 4.5 offer a 1M-token context window in public preview for large-scale code analysis. AgentCore uses a Recursive LLM architecture that decouples document size from context window size.

AWS reported a 100% success rate on the Code Repository Understanding subset of LongBench v2 versus 46.7% for the base approach, though that result covers a specific task set and is separate from a standardized coding agent benchmark. Bedrock's multi-agent architecture decomposes tasks across specialized agents rather than providing native cross-repository semantic understanding.

In a Cosmos Deep Code Review test on a multi-service pull request, the results matched the 59% F-score profile, including the 65% precision and 55% recall tradeoff. The reviewer followed dependency chains and related tests with broader codebase context instead of reviewing the diff alone. Cosmos serves as the recall-first option for large or multi-service codebases where a missed cross-service integration bug carries production cost. Context Engine supports multi-repo indexing across GitHub natively, and GitLab and Bitbucket via CLI-based CI/CD integration, with auto-sync on every push.

Treat vendor-operated benchmark figures cautiously. Cosmos's self-reported SWE-bench Verified figure is 70.6% on current materials, though a March 31, 2025 benchmark post cited a 65.4% success rate, and code review quality is separately reported at 59% F-score. Bedrock Agents has no published SWE-bench score as a coding system in available AWS sources. Engineering leaders should weight vendor-operated benchmarks at a discount to third-party evaluations, and note that Anthropic's June 2026 launch materials list Fable 5 at 95.0% SWE-bench Verified and 80.3% SWE-bench Pro.

[ Free report ]

The Agentic SDLC

How teams like Stripe, Ramp, and Uber move from solo coding agents to a coordinated, team-level system.

Download the guide

What Are the Key Limitations of Each Platform?

Bedrock requires explicit prepare-agent refreshes after schema changes, doesn't expose cost-per-conversation as a first-class CloudWatch metric, and adds cold-start latency for AgentCore beyond its initial warm pool. Cosmos requires initial indexing for 400,000+ file codebases, lacks offline capability, starts at a $100/month entry floor, and had no independent third-party coverage found in available public sources after its June 2026 GA.

Bedrock's documented limitations include several operational sharp edges. After action group schema changes, agents require the explicit prepare-agent call to refresh the schema cache; AWS's own testing guidance recommends checking the agent's last-prepared time before every test run, since testing against a stale configuration is an easy mistake to make otherwise. Native observability through CloudWatch provides metrics such as session count, latency, duration, token usage, and error rates, but cost-per-conversation isn't exposed as a first-class metric. AgentCore cold starts add measurable latency beyond the initial warm-pool capacity, though mitigation strategies like pre-warming and multiple endpoints reduce the impact.

Cosmos limitations reflect its public preview and early launch state in June 2026, before it became generally available. Four constraints affect adoption effort, offline requirements, and procurement risk.

• Large codebases require an initial indexing pass, and the platform has no offline mode.
• The flat Business plan resolves earlier credit-system confusion but raises the entry price floor to $100/month.
• A review bottleneck of 1,400 open PRs at one point was part of the impetus for building Cosmos.
• Advisor limitations include two setup steps still requiring manual web UI completion.

Rollout plans should account for these constraints before either platform reaches production use.

One caveat applies to both and to any AI coding platform. A METR randomized controlled trial covering early-2025 tools found developers using AI took 19% longer while believing they were 20% faster; a February 2026 METR follow-up suggests speedups have likely improved since then, though METR itself flags the newer data as weak evidence given selection effects in the follow-up sample. The 2025 DORA report found a related pattern industry-wide: AI adoption correlates with higher throughput but also higher delivery instability. Model both dynamics into your change failure rate and MTTR expectations before presenting ROI projections.

Match the Platform to the Workflow

Choose based on whether the team needs to build AI applications or run software-development agents. Teams building AI-powered applications and business automations for end users on AWS should assess AgentCore. Key evaluation areas include IAM, Lambda, S3, DynamoDB, CloudWatch, KMS, Bedrock model access controls, and GitHub Actions OIDC deployment. For SDLC workflows across large multi-repo systems, evaluate Cosmos through a Context Engine that reasons over 400,000+ files.

Pilot one ticket through planning, code review, testing, and incident-style remediation. Then compare handoff count, review quality, compliance fit, and multi-turn cost. Cosmos coordinates review, testing, PR authoring, and incident response against the real codebase with shared memory across sessions and agents.

Frequently Asked Questions

Related Reading

• OpenAI Codex vs Augment Cosmos: AI Coding Compared
• Google Antigravity vs Augment Cosmos: Multi-Agent Platforms
• GitHub Copilot Agent Mode vs Augment Cosmos: Where Agent Coordination Lives
• Cursor Background Agents vs Cosmos: IDE vs Agentic OS
• Factory AI vs Augment Cosmos: Which Agent Layer Do You Actually Need?